prompt
listlengths 2
2
| adversarial
bool 1
class | source
stringclasses 1
value | conv_id
stringlengths 6
8
| unique_conv_id
stringlengths 11
13
| turn_id
int64 1
1
| data_source
stringclasses 1
value | ability
stringclasses 1
value | reward_model
dict | extra_info
dict |
|---|---|---|---|---|---|---|---|---|---|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: The following subproblems refer to the exponential function $e^{-t / 2} \\cos (3 t)$, which we will assume is a solution of the differential equation $m \\ddot{x}+b \\dot{x}+k x=0$. \n\nWhat is $b$ in terms of $m$? Write $b$ as a constant times a function of $m$.",
"role": "user"
}
] | false
|
minerva
|
conv_100
|
uniq_conv_100
| 1
|
minerva
|
minerva
|
{
"ground_truth": "m",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: The following subproblems refer to the exponential function $e^{-t / 2} \\cos (3 t)$, which we will assume is a solution of the differential equation $m \\ddot{x}+b \\dot{x}+k x=0$. \n\nWhat is $b$ in terms of $m$? Write $b$ as a constant times a function of $m$.",
"role": "user"
}
],
"index": 100,
"single_turn_completion": "m",
"single_turn_prompt": "Preamble: The following subproblems refer to the exponential function $e^{-t / 2} \\cos (3 t)$, which we will assume is a solution of the differential equation $m \\ddot{x}+b \\dot{x}+k x=0$. \n\nWhat is $b$ in terms of $m$? Write $b$ as a constant times a function of $m$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: The following subproblems refer to the differential equation. $\\ddot{x}+4 x=\\sin (3 t)$\n\nSubproblem 0: Find $A$ so that $A \\sin (3 t)$ is a solution of $\\ddot{x}+4 x=\\sin (3 t)$.\n\n\nSolution: We can find this by brute force. If $x=A \\sin (3 t)$, then $\\ddot{x}=-9 A \\sin (3 t)$, so $\\ddot{x}+4 x=-5 A \\sin (3 t)$. Therefore, when $A=\\boxed{-0.2}, x_{p}(t)=-\\sin (3 t) / 5$ is a solution of the given equation.\n\nFinal answer: The final answer is -0.2. I hope it is correct.\n\nSubproblem 1: What is the general solution, in the form $f_0(t) + C_1f_1(t) + C_2f_2(t)$, where $C_1, C_2$ denote arbitrary constants?",
"role": "user"
}
] | false
|
minerva
|
conv_101
|
uniq_conv_101
| 1
|
minerva
|
minerva
|
{
"ground_truth": "-\\sin (3 t) / 5+ C_{1} \\sin (2 t)+C_{2} \\cos (2 t)",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: The following subproblems refer to the differential equation. $\\ddot{x}+4 x=\\sin (3 t)$\n\nSubproblem 0: Find $A$ so that $A \\sin (3 t)$ is a solution of $\\ddot{x}+4 x=\\sin (3 t)$.\n\n\nSolution: We can find this by brute force. If $x=A \\sin (3 t)$, then $\\ddot{x}=-9 A \\sin (3 t)$, so $\\ddot{x}+4 x=-5 A \\sin (3 t)$. Therefore, when $A=\\boxed{-0.2}, x_{p}(t)=-\\sin (3 t) / 5$ is a solution of the given equation.\n\nFinal answer: The final answer is -0.2. I hope it is correct.\n\nSubproblem 1: What is the general solution, in the form $f_0(t) + C_1f_1(t) + C_2f_2(t)$, where $C_1, C_2$ denote arbitrary constants?",
"role": "user"
}
],
"index": 101,
"single_turn_completion": "-\\sin (3 t) / 5+ C_{1} \\sin (2 t)+C_{2} \\cos (2 t)",
"single_turn_prompt": "Preamble: The following subproblems refer to the differential equation. $\\ddot{x}+4 x=\\sin (3 t)$\n\nSubproblem 0: Find $A$ so that $A \\sin (3 t)$ is a solution of $\\ddot{x}+4 x=\\sin (3 t)$.\n\n\nSolution: We can find this by brute force. If $x=A \\sin (3 t)$, then $\\ddot{x}=-9 A \\sin (3 t)$, so $\\ddot{x}+4 x=-5 A \\sin (3 t)$. Therefore, when $A=\\boxed{-0.2}, x_{p}(t)=-\\sin (3 t) / 5$ is a solution of the given equation.\n\nFinal answer: The final answer is -0.2. I hope it is correct.\n\nSubproblem 1: What is the general solution, in the form $f_0(t) + C_1f_1(t) + C_2f_2(t)$, where $C_1, C_2$ denote arbitrary constants?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: What is the smallest possible positive $k$ such that all functions $x(t)=A \\cos (\\omega t-\\phi)$---where $\\phi$ is an odd multiple of $k$---satisfy $x(0)=0$? \\\\",
"role": "user"
}
] | false
|
minerva
|
conv_102
|
uniq_conv_102
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{\\pi}{2}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "What is the smallest possible positive $k$ such that all functions $x(t)=A \\cos (\\omega t-\\phi)$---where $\\phi$ is an odd multiple of $k$---satisfy $x(0)=0$? \\\\",
"role": "user"
}
],
"index": 102,
"single_turn_completion": "\\frac{\\pi}{2}",
"single_turn_prompt": "What is the smallest possible positive $k$ such that all functions $x(t)=A \\cos (\\omega t-\\phi)$---where $\\phi$ is an odd multiple of $k$---satisfy $x(0)=0$? \\\\",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: The following subproblems refer to the differential equation $\\ddot{x}+b \\dot{x}+x=0$.\\\\\n\nWhat is the characteristic polynomial $p(s)$ of $\\ddot{x}+b \\dot{x}+x=0$?",
"role": "user"
}
] | false
|
minerva
|
conv_103
|
uniq_conv_103
| 1
|
minerva
|
minerva
|
{
"ground_truth": "s^{2}+b s+1",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: The following subproblems refer to the differential equation $\\ddot{x}+b \\dot{x}+x=0$.\\\\\n\nWhat is the characteristic polynomial $p(s)$ of $\\ddot{x}+b \\dot{x}+x=0$?",
"role": "user"
}
],
"index": 103,
"single_turn_completion": "s^{2}+b s+1",
"single_turn_prompt": "Preamble: The following subproblems refer to the differential equation $\\ddot{x}+b \\dot{x}+x=0$.\\\\\n\nWhat is the characteristic polynomial $p(s)$ of $\\ddot{x}+b \\dot{x}+x=0$?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: The following subproblems refer to the exponential function $e^{-t / 2} \\cos (3 t)$, which we will assume is a solution of the differential equation $m \\ddot{x}+b \\dot{x}+k x=0$. \n\nSubproblem 0: What is $b$ in terms of $m$? Write $b$ as a constant times a function of $m$.\n\n\nSolution: We can write $e^{-t / 2} \\cos (3 t)=\\operatorname{Re} e^{(-1 / 2 \\pm 3 i) t}$, so $p(s)=m s^{2}+b s+k$ has solutions $-\\frac{1}{2} \\pm 3 i$. This means $p(s)=m(s+1 / 2-3 i)(s+1 / 2+3 i)=m\\left(s^{2}+s+\\frac{37}{4}\\right)$. Then $b=\\boxed{m}$, \n\nFinal answer: The final answer is m. I hope it is correct.\n\nSubproblem 1: What is $k$ in terms of $m$? Write $k$ as a constant times a function of $m$.",
"role": "user"
}
] | false
|
minerva
|
conv_104
|
uniq_conv_104
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{37}{4} m",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: The following subproblems refer to the exponential function $e^{-t / 2} \\cos (3 t)$, which we will assume is a solution of the differential equation $m \\ddot{x}+b \\dot{x}+k x=0$. \n\nSubproblem 0: What is $b$ in terms of $m$? Write $b$ as a constant times a function of $m$.\n\n\nSolution: We can write $e^{-t / 2} \\cos (3 t)=\\operatorname{Re} e^{(-1 / 2 \\pm 3 i) t}$, so $p(s)=m s^{2}+b s+k$ has solutions $-\\frac{1}{2} \\pm 3 i$. This means $p(s)=m(s+1 / 2-3 i)(s+1 / 2+3 i)=m\\left(s^{2}+s+\\frac{37}{4}\\right)$. Then $b=\\boxed{m}$, \n\nFinal answer: The final answer is m. I hope it is correct.\n\nSubproblem 1: What is $k$ in terms of $m$? Write $k$ as a constant times a function of $m$.",
"role": "user"
}
],
"index": 104,
"single_turn_completion": "\\frac{37}{4} m",
"single_turn_prompt": "Preamble: The following subproblems refer to the exponential function $e^{-t / 2} \\cos (3 t)$, which we will assume is a solution of the differential equation $m \\ddot{x}+b \\dot{x}+k x=0$. \n\nSubproblem 0: What is $b$ in terms of $m$? Write $b$ as a constant times a function of $m$.\n\n\nSolution: We can write $e^{-t / 2} \\cos (3 t)=\\operatorname{Re} e^{(-1 / 2 \\pm 3 i) t}$, so $p(s)=m s^{2}+b s+k$ has solutions $-\\frac{1}{2} \\pm 3 i$. This means $p(s)=m(s+1 / 2-3 i)(s+1 / 2+3 i)=m\\left(s^{2}+s+\\frac{37}{4}\\right)$. Then $b=\\boxed{m}$, \n\nFinal answer: The final answer is m. I hope it is correct.\n\nSubproblem 1: What is $k$ in terms of $m$? Write $k$ as a constant times a function of $m$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: In the following problems, take $a = \\ln 2$ and $b = \\pi / 3$. \n\nSubproblem 0: Given $a = \\ln 2$ and $b = \\pi / 3$, rewrite $e^{a+b i}$ in the form $x + yi$, where $x, y$ are real numbers. \n\n\nSolution: Using Euler's formula, we find that the answer is $\\boxed{1+\\sqrt{3} i}$.\n\nFinal answer: The final answer is 1+\\sqrt{3} i. I hope it is correct.\n\nSubproblem 1: Given $a = \\ln 2$ and $b = \\pi / 3$, rewrite $e^{2(a+b i)}$ in the form $x + yi$, where $x, y$ are real numbers.\n\n\nSolution: $e^{n(a+b i)}=(1+\\sqrt{3} i)^{n}$, so the answer is $\\boxed{-2+2 \\sqrt{3} i}$.\n\nFinal answer: The final answer is -2+2 \\sqrt{3} i. I hope it is correct.\n\nSubproblem 2: Rewrite $e^{3(a+b i)}$ in the form $x + yi$, where $x, y$ are real numbers. \n\n\nSolution: $e^{n(a+b i)}=(1+\\sqrt{3} i)^{n}$, so the answer is $\\boxed{-8}$.\n\nFinal answer: The final answer is -8. I hope it is correct.\n\nSubproblem 3: Rewrite $e^{4(a+b i)}$ in the form $x + yi$, where $x, y$ are real numbers.",
"role": "user"
}
] | false
|
minerva
|
conv_105
|
uniq_conv_105
| 1
|
minerva
|
minerva
|
{
"ground_truth": "-8-8 \\sqrt{3} i",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: In the following problems, take $a = \\ln 2$ and $b = \\pi / 3$. \n\nSubproblem 0: Given $a = \\ln 2$ and $b = \\pi / 3$, rewrite $e^{a+b i}$ in the form $x + yi$, where $x, y$ are real numbers. \n\n\nSolution: Using Euler's formula, we find that the answer is $\\boxed{1+\\sqrt{3} i}$.\n\nFinal answer: The final answer is 1+\\sqrt{3} i. I hope it is correct.\n\nSubproblem 1: Given $a = \\ln 2$ and $b = \\pi / 3$, rewrite $e^{2(a+b i)}$ in the form $x + yi$, where $x, y$ are real numbers.\n\n\nSolution: $e^{n(a+b i)}=(1+\\sqrt{3} i)^{n}$, so the answer is $\\boxed{-2+2 \\sqrt{3} i}$.\n\nFinal answer: The final answer is -2+2 \\sqrt{3} i. I hope it is correct.\n\nSubproblem 2: Rewrite $e^{3(a+b i)}$ in the form $x + yi$, where $x, y$ are real numbers. \n\n\nSolution: $e^{n(a+b i)}=(1+\\sqrt{3} i)^{n}$, so the answer is $\\boxed{-8}$.\n\nFinal answer: The final answer is -8. I hope it is correct.\n\nSubproblem 3: Rewrite $e^{4(a+b i)}$ in the form $x + yi$, where $x, y$ are real numbers.",
"role": "user"
}
],
"index": 105,
"single_turn_completion": "-8-8 \\sqrt{3} i",
"single_turn_prompt": "Preamble: In the following problems, take $a = \\ln 2$ and $b = \\pi / 3$. \n\nSubproblem 0: Given $a = \\ln 2$ and $b = \\pi / 3$, rewrite $e^{a+b i}$ in the form $x + yi$, where $x, y$ are real numbers. \n\n\nSolution: Using Euler's formula, we find that the answer is $\\boxed{1+\\sqrt{3} i}$.\n\nFinal answer: The final answer is 1+\\sqrt{3} i. I hope it is correct.\n\nSubproblem 1: Given $a = \\ln 2$ and $b = \\pi / 3$, rewrite $e^{2(a+b i)}$ in the form $x + yi$, where $x, y$ are real numbers.\n\n\nSolution: $e^{n(a+b i)}=(1+\\sqrt{3} i)^{n}$, so the answer is $\\boxed{-2+2 \\sqrt{3} i}$.\n\nFinal answer: The final answer is -2+2 \\sqrt{3} i. I hope it is correct.\n\nSubproblem 2: Rewrite $e^{3(a+b i)}$ in the form $x + yi$, where $x, y$ are real numbers. \n\n\nSolution: $e^{n(a+b i)}=(1+\\sqrt{3} i)^{n}$, so the answer is $\\boxed{-8}$.\n\nFinal answer: The final answer is -8. I hope it is correct.\n\nSubproblem 3: Rewrite $e^{4(a+b i)}$ in the form $x + yi$, where $x, y$ are real numbers.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Rewrite the function $\\operatorname{Re} \\frac{e^{i t}}{2+2 i}$ in the form $A \\cos (\\omega t-\\phi)$. It may help to begin by drawing a right triangle with sides $a$ and $b$.",
"role": "user"
}
] | false
|
minerva
|
conv_106
|
uniq_conv_106
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{\\sqrt{2}}{4} \\cos (t-\\pi / 4)",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Rewrite the function $\\operatorname{Re} \\frac{e^{i t}}{2+2 i}$ in the form $A \\cos (\\omega t-\\phi)$. It may help to begin by drawing a right triangle with sides $a$ and $b$.",
"role": "user"
}
],
"index": 106,
"single_turn_completion": "\\frac{\\sqrt{2}}{4} \\cos (t-\\pi / 4)",
"single_turn_prompt": "Rewrite the function $\\operatorname{Re} \\frac{e^{i t}}{2+2 i}$ in the form $A \\cos (\\omega t-\\phi)$. It may help to begin by drawing a right triangle with sides $a$ and $b$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: The following subproblems refer to the differential equation $\\ddot{x}+b \\dot{x}+x=0$.\\\\\n\nSubproblem 0: What is the characteristic polynomial $p(s)$ of $\\ddot{x}+b \\dot{x}+x=0$?\n\n\nSolution: The characteristic polynomial is $p(s)=\\boxed{s^{2}+b s+1}$.\n\nFinal answer: The final answer is s^{2}+b s+1. I hope it is correct.\n\nSubproblem 1: For what value of $b$ does $\\ddot{x}+b \\dot{x}+x=0$ exhibit critical damping?",
"role": "user"
}
] | false
|
minerva
|
conv_107
|
uniq_conv_107
| 1
|
minerva
|
minerva
|
{
"ground_truth": "2",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: The following subproblems refer to the differential equation $\\ddot{x}+b \\dot{x}+x=0$.\\\\\n\nSubproblem 0: What is the characteristic polynomial $p(s)$ of $\\ddot{x}+b \\dot{x}+x=0$?\n\n\nSolution: The characteristic polynomial is $p(s)=\\boxed{s^{2}+b s+1}$.\n\nFinal answer: The final answer is s^{2}+b s+1. I hope it is correct.\n\nSubproblem 1: For what value of $b$ does $\\ddot{x}+b \\dot{x}+x=0$ exhibit critical damping?",
"role": "user"
}
],
"index": 107,
"single_turn_completion": "2",
"single_turn_prompt": "Preamble: The following subproblems refer to the differential equation $\\ddot{x}+b \\dot{x}+x=0$.\\\\\n\nSubproblem 0: What is the characteristic polynomial $p(s)$ of $\\ddot{x}+b \\dot{x}+x=0$?\n\n\nSolution: The characteristic polynomial is $p(s)=\\boxed{s^{2}+b s+1}$.\n\nFinal answer: The final answer is s^{2}+b s+1. I hope it is correct.\n\nSubproblem 1: For what value of $b$ does $\\ddot{x}+b \\dot{x}+x=0$ exhibit critical damping?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Find the general (complex-valued) solution of the differential equation $\\dot{z}+2 z=e^{2 i t}$, using $C$ to stand for any complex-valued integration constants which may arise.",
"role": "user"
}
] | false
|
minerva
|
conv_108
|
uniq_conv_108
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{e^{2 i t}}{(2+2 i)}+C e^{-2 t}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Find the general (complex-valued) solution of the differential equation $\\dot{z}+2 z=e^{2 i t}$, using $C$ to stand for any complex-valued integration constants which may arise.",
"role": "user"
}
],
"index": 108,
"single_turn_completion": "\\frac{e^{2 i t}}{(2+2 i)}+C e^{-2 t}",
"single_turn_prompt": "Find the general (complex-valued) solution of the differential equation $\\dot{z}+2 z=e^{2 i t}$, using $C$ to stand for any complex-valued integration constants which may arise.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Consider the first-order system\n\\[\n\\tau \\dot{y}+y=u\n\\]\ndriven with a unit step from zero initial conditions. The input to this system is \\(u\\) and the output is \\(y\\). \n\nDerive and expression for the settling time \\(t_{s}\\), where the settling is to within an error \\(\\pm \\Delta\\) from the final value of 1.",
"role": "user"
}
] | false
|
minerva
|
conv_109
|
uniq_conv_109
| 1
|
minerva
|
minerva
|
{
"ground_truth": "-\\tau \\ln \\Delta",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Consider the first-order system\n\\[\n\\tau \\dot{y}+y=u\n\\]\ndriven with a unit step from zero initial conditions. The input to this system is \\(u\\) and the output is \\(y\\). \n\nDerive and expression for the settling time \\(t_{s}\\), where the settling is to within an error \\(\\pm \\Delta\\) from the final value of 1.",
"role": "user"
}
],
"index": 109,
"single_turn_completion": "-\\tau \\ln \\Delta",
"single_turn_prompt": "Preamble: Consider the first-order system\n\\[\n\\tau \\dot{y}+y=u\n\\]\ndriven with a unit step from zero initial conditions. The input to this system is \\(u\\) and the output is \\(y\\). \n\nDerive and expression for the settling time \\(t_{s}\\), where the settling is to within an error \\(\\pm \\Delta\\) from the final value of 1.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Consider the first-order system\n\\[\n\\tau \\dot{y}+y=u\n\\]\ndriven with a unit step from zero initial conditions. The input to this system is \\(u\\) and the output is \\(y\\). \n\nSubproblem 0: Derive and expression for the settling time \\(t_{s}\\), where the settling is to within an error \\(\\pm \\Delta\\) from the final value of 1.\n\n\nSolution: Rise and Settling Times. We are given the first-order transfer function\n\\[\nH(s)=\\frac{1}{\\tau s+1}\n\\]\nThe response to a unit step with zero initial conditions will be \\(y(t)=1-e^{-t / \\tau}\\). To determine the amount of time it take \\(y\\) to settle to within \\(\\Delta\\) of its final value, we want to find the time \\(t_{s}\\) such that \\(y\\left(t_{s}\\right)=1-\\Delta\\). Thus, we obtain\n\\[\n\\begin{aligned}\n&\\Delta=e^{-t_{s} / \\tau} \\\\\n&t_{s}=\\boxed{-\\tau \\ln \\Delta}\n\\end{aligned}\n\\]\n\nFinal answer: The final answer is -\\tau \\ln \\Delta. I hope it is correct.\n\nSubproblem 1: Derive an expression for the \\(10-90 \\%\\) rise time \\(t_{r}\\) in terms of $\\tau$.",
"role": "user"
}
] | false
|
minerva
|
conv_110
|
uniq_conv_110
| 1
|
minerva
|
minerva
|
{
"ground_truth": "2.2 \\tau",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Consider the first-order system\n\\[\n\\tau \\dot{y}+y=u\n\\]\ndriven with a unit step from zero initial conditions. The input to this system is \\(u\\) and the output is \\(y\\). \n\nSubproblem 0: Derive and expression for the settling time \\(t_{s}\\), where the settling is to within an error \\(\\pm \\Delta\\) from the final value of 1.\n\n\nSolution: Rise and Settling Times. We are given the first-order transfer function\n\\[\nH(s)=\\frac{1}{\\tau s+1}\n\\]\nThe response to a unit step with zero initial conditions will be \\(y(t)=1-e^{-t / \\tau}\\). To determine the amount of time it take \\(y\\) to settle to within \\(\\Delta\\) of its final value, we want to find the time \\(t_{s}\\) such that \\(y\\left(t_{s}\\right)=1-\\Delta\\). Thus, we obtain\n\\[\n\\begin{aligned}\n&\\Delta=e^{-t_{s} / \\tau} \\\\\n&t_{s}=\\boxed{-\\tau \\ln \\Delta}\n\\end{aligned}\n\\]\n\nFinal answer: The final answer is -\\tau \\ln \\Delta. I hope it is correct.\n\nSubproblem 1: Derive an expression for the \\(10-90 \\%\\) rise time \\(t_{r}\\) in terms of $\\tau$.",
"role": "user"
}
],
"index": 110,
"single_turn_completion": "2.2 \\tau",
"single_turn_prompt": "Preamble: Consider the first-order system\n\\[\n\\tau \\dot{y}+y=u\n\\]\ndriven with a unit step from zero initial conditions. The input to this system is \\(u\\) and the output is \\(y\\). \n\nSubproblem 0: Derive and expression for the settling time \\(t_{s}\\), where the settling is to within an error \\(\\pm \\Delta\\) from the final value of 1.\n\n\nSolution: Rise and Settling Times. We are given the first-order transfer function\n\\[\nH(s)=\\frac{1}{\\tau s+1}\n\\]\nThe response to a unit step with zero initial conditions will be \\(y(t)=1-e^{-t / \\tau}\\). To determine the amount of time it take \\(y\\) to settle to within \\(\\Delta\\) of its final value, we want to find the time \\(t_{s}\\) such that \\(y\\left(t_{s}\\right)=1-\\Delta\\). Thus, we obtain\n\\[\n\\begin{aligned}\n&\\Delta=e^{-t_{s} / \\tau} \\\\\n&t_{s}=\\boxed{-\\tau \\ln \\Delta}\n\\end{aligned}\n\\]\n\nFinal answer: The final answer is -\\tau \\ln \\Delta. I hope it is correct.\n\nSubproblem 1: Derive an expression for the \\(10-90 \\%\\) rise time \\(t_{r}\\) in terms of $\\tau$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For each of the functions $y(t)$, find the Laplace Transform $Y(s)$ :\n\n$y(t)=e^{-a t}$",
"role": "user"
}
] | false
|
minerva
|
conv_111
|
uniq_conv_111
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{1}{s+a}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For each of the functions $y(t)$, find the Laplace Transform $Y(s)$ :\n\n$y(t)=e^{-a t}$",
"role": "user"
}
],
"index": 111,
"single_turn_completion": "\\frac{1}{s+a}",
"single_turn_prompt": "Preamble: For each of the functions $y(t)$, find the Laplace Transform $Y(s)$ :\n\n$y(t)=e^{-a t}$",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For each Laplace Transform \\(Y(s)\\), find the function \\(y(t)\\) :\n\nSubproblem 0: \\[\nY(s)=\\boxed{\\frac{1}{(s+a)(s+b)}}\n\\]\n\n\nSolution: We can simplify with partial fractions:\n\\[\nY(s)=\\frac{1}{(s+a)(s+b)}=\\frac{C}{s+a}+\\frac{D}{s+b}\n\\]\nfind the constants \\(C\\) and \\(D\\) by setting \\(s=-a\\) and \\(s=-b\\)\n\\[\n\\begin{aligned}\n\\frac{1}{(s+a)(s+b)} &=\\frac{C}{s+a}+\\frac{D}{s+b} \\\\\n1 &=C(s+b)+D(s+a) \\\\\nC &=\\frac{1}{b-a} \\\\\nD &=\\frac{1}{a-b}\n\\end{aligned}\n\\]\ntherefore\n\\[\nY(s)=\\frac{1}{b-a} \\frac{1}{s+a}-\\frac{1}{b-a} \\frac{1}{s+b}\n\\]\nBy looking up the inverse Laplace Transform of \\(\\frac{1}{s+b}\\), we find the total solution \\(y(t)\\)\n\\[\ny(t)=\\boxed{\\frac{1}{b-a}\\left(e^{-a t}-e^{-b t}\\right)}\n\\]\n\nFinal answer: The final answer is \\frac{1}{b-a}\\left(e^{-a t}-e^{-b t}\\right). I hope it is correct.\n\nSubproblem 1: \\[\nY(s)=\\frac{s}{\\frac{s^{2}}{\\omega_{n}^{2}}+\\frac{2 \\zeta}{\\omega_{n}} s+1}\n\\]\nYou may assume that $\\zeta < 1$.",
"role": "user"
}
] | false
|
minerva
|
conv_112
|
uniq_conv_112
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\omega_{n}^{2} e^{-\\zeta \\omega_{n} t} \\cos \\left(\\omega_{n} \\sqrt{1-\\zeta^{2}} t\\right)-\\frac{\\zeta \\omega_{n}^{2}}{\\sqrt{1-\\zeta^{2}}} e^{-\\zeta \\omega_{n} t} \\sin \\left(\\omega_{n} \\sqrt{1-\\zeta^{2}} t\\right)",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For each Laplace Transform \\(Y(s)\\), find the function \\(y(t)\\) :\n\nSubproblem 0: \\[\nY(s)=\\boxed{\\frac{1}{(s+a)(s+b)}}\n\\]\n\n\nSolution: We can simplify with partial fractions:\n\\[\nY(s)=\\frac{1}{(s+a)(s+b)}=\\frac{C}{s+a}+\\frac{D}{s+b}\n\\]\nfind the constants \\(C\\) and \\(D\\) by setting \\(s=-a\\) and \\(s=-b\\)\n\\[\n\\begin{aligned}\n\\frac{1}{(s+a)(s+b)} &=\\frac{C}{s+a}+\\frac{D}{s+b} \\\\\n1 &=C(s+b)+D(s+a) \\\\\nC &=\\frac{1}{b-a} \\\\\nD &=\\frac{1}{a-b}\n\\end{aligned}\n\\]\ntherefore\n\\[\nY(s)=\\frac{1}{b-a} \\frac{1}{s+a}-\\frac{1}{b-a} \\frac{1}{s+b}\n\\]\nBy looking up the inverse Laplace Transform of \\(\\frac{1}{s+b}\\), we find the total solution \\(y(t)\\)\n\\[\ny(t)=\\boxed{\\frac{1}{b-a}\\left(e^{-a t}-e^{-b t}\\right)}\n\\]\n\nFinal answer: The final answer is \\frac{1}{b-a}\\left(e^{-a t}-e^{-b t}\\right). I hope it is correct.\n\nSubproblem 1: \\[\nY(s)=\\frac{s}{\\frac{s^{2}}{\\omega_{n}^{2}}+\\frac{2 \\zeta}{\\omega_{n}} s+1}\n\\]\nYou may assume that $\\zeta < 1$.",
"role": "user"
}
],
"index": 112,
"single_turn_completion": "\\omega_{n}^{2} e^{-\\zeta \\omega_{n} t} \\cos \\left(\\omega_{n} \\sqrt{1-\\zeta^{2}} t\\right)-\\frac{\\zeta \\omega_{n}^{2}}{\\sqrt{1-\\zeta^{2}}} e^{-\\zeta \\omega_{n} t} \\sin \\left(\\omega_{n} \\sqrt{1-\\zeta^{2}} t\\right)",
"single_turn_prompt": "Preamble: For each Laplace Transform \\(Y(s)\\), find the function \\(y(t)\\) :\n\nSubproblem 0: \\[\nY(s)=\\boxed{\\frac{1}{(s+a)(s+b)}}\n\\]\n\n\nSolution: We can simplify with partial fractions:\n\\[\nY(s)=\\frac{1}{(s+a)(s+b)}=\\frac{C}{s+a}+\\frac{D}{s+b}\n\\]\nfind the constants \\(C\\) and \\(D\\) by setting \\(s=-a\\) and \\(s=-b\\)\n\\[\n\\begin{aligned}\n\\frac{1}{(s+a)(s+b)} &=\\frac{C}{s+a}+\\frac{D}{s+b} \\\\\n1 &=C(s+b)+D(s+a) \\\\\nC &=\\frac{1}{b-a} \\\\\nD &=\\frac{1}{a-b}\n\\end{aligned}\n\\]\ntherefore\n\\[\nY(s)=\\frac{1}{b-a} \\frac{1}{s+a}-\\frac{1}{b-a} \\frac{1}{s+b}\n\\]\nBy looking up the inverse Laplace Transform of \\(\\frac{1}{s+b}\\), we find the total solution \\(y(t)\\)\n\\[\ny(t)=\\boxed{\\frac{1}{b-a}\\left(e^{-a t}-e^{-b t}\\right)}\n\\]\n\nFinal answer: The final answer is \\frac{1}{b-a}\\left(e^{-a t}-e^{-b t}\\right). I hope it is correct.\n\nSubproblem 1: \\[\nY(s)=\\frac{s}{\\frac{s^{2}}{\\omega_{n}^{2}}+\\frac{2 \\zeta}{\\omega_{n}} s+1}\n\\]\nYou may assume that $\\zeta < 1$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: A signal \\(x(t)\\) is given by\n\\[\nx(t)=\\left(e^{-t}-e^{-1}\\right)\\left(u_{s}(t)-u_{s}(t-1)\\right)\n\\]\nCalculate its Laplace transform \\(X(s)\\). Make sure to clearly show the steps in your calculation.",
"role": "user"
}
] | false
|
minerva
|
conv_113
|
uniq_conv_113
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{1}{s+1}-\\frac{e^{-1}}{s}-\\frac{e^{-1} e^{-s}}{s+1}+\\frac{e^{-1} e^{-s}}{s}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A signal \\(x(t)\\) is given by\n\\[\nx(t)=\\left(e^{-t}-e^{-1}\\right)\\left(u_{s}(t)-u_{s}(t-1)\\right)\n\\]\nCalculate its Laplace transform \\(X(s)\\). Make sure to clearly show the steps in your calculation.",
"role": "user"
}
],
"index": 113,
"single_turn_completion": "\\frac{1}{s+1}-\\frac{e^{-1}}{s}-\\frac{e^{-1} e^{-s}}{s+1}+\\frac{e^{-1} e^{-s}}{s}",
"single_turn_prompt": "A signal \\(x(t)\\) is given by\n\\[\nx(t)=\\left(e^{-t}-e^{-1}\\right)\\left(u_{s}(t)-u_{s}(t-1)\\right)\n\\]\nCalculate its Laplace transform \\(X(s)\\). Make sure to clearly show the steps in your calculation.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: You are given an equation of motion of the form:\n\\[\n\\dot{y}+5 y=10 u\n\\]\n\nSubproblem 0: What is the time constant for this system?\n\n\nSolution: We find the homogenous solution, solving:\n\\[\n\\dot{y}+5 y=0\n\\]\nby trying a solution of the form $y=A \\cdot e^{s, t}$.\nCalculation:\n\\[\n\\dot{y}=A \\cdot s \\cdot e^{s \\cdot t} \\mid \\Rightarrow A \\cdot s \\cdot e^{s t}+5 A \\cdot e^{s t}=0\n\\]\nyields that $s=-5$, meaning the solution is $y=A \\cdot e^{-5 \\cdot t}=A \\cdot e^{-t / \\tau}$, meaning $\\tau = \\boxed{0.2}$.\n\nFinal answer: The final answer is 0.2. I hope it is correct.\n\nSubproblem 1: If \\(u=10\\), what is the final or steady-state value for \\(y(t)\\)?",
"role": "user"
}
] | false
|
minerva
|
conv_114
|
uniq_conv_114
| 1
|
minerva
|
minerva
|
{
"ground_truth": "20",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: You are given an equation of motion of the form:\n\\[\n\\dot{y}+5 y=10 u\n\\]\n\nSubproblem 0: What is the time constant for this system?\n\n\nSolution: We find the homogenous solution, solving:\n\\[\n\\dot{y}+5 y=0\n\\]\nby trying a solution of the form $y=A \\cdot e^{s, t}$.\nCalculation:\n\\[\n\\dot{y}=A \\cdot s \\cdot e^{s \\cdot t} \\mid \\Rightarrow A \\cdot s \\cdot e^{s t}+5 A \\cdot e^{s t}=0\n\\]\nyields that $s=-5$, meaning the solution is $y=A \\cdot e^{-5 \\cdot t}=A \\cdot e^{-t / \\tau}$, meaning $\\tau = \\boxed{0.2}$.\n\nFinal answer: The final answer is 0.2. I hope it is correct.\n\nSubproblem 1: If \\(u=10\\), what is the final or steady-state value for \\(y(t)\\)?",
"role": "user"
}
],
"index": 114,
"single_turn_completion": "20",
"single_turn_prompt": "Preamble: You are given an equation of motion of the form:\n\\[\n\\dot{y}+5 y=10 u\n\\]\n\nSubproblem 0: What is the time constant for this system?\n\n\nSolution: We find the homogenous solution, solving:\n\\[\n\\dot{y}+5 y=0\n\\]\nby trying a solution of the form $y=A \\cdot e^{s, t}$.\nCalculation:\n\\[\n\\dot{y}=A \\cdot s \\cdot e^{s \\cdot t} \\mid \\Rightarrow A \\cdot s \\cdot e^{s t}+5 A \\cdot e^{s t}=0\n\\]\nyields that $s=-5$, meaning the solution is $y=A \\cdot e^{-5 \\cdot t}=A \\cdot e^{-t / \\tau}$, meaning $\\tau = \\boxed{0.2}$.\n\nFinal answer: The final answer is 0.2. I hope it is correct.\n\nSubproblem 1: If \\(u=10\\), what is the final or steady-state value for \\(y(t)\\)?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: A signal \\(w(t)\\) is defined as\n\\[\nw(t)=u_{s}(t)-u_{s}(t-T)\n\\]\nwhere \\(T\\) is a fixed time in seconds and \\(u_{s}(t)\\) is the unit step. Compute the Laplace transform \\(W(s)\\) of \\(w(t)\\). Show your work.",
"role": "user"
}
] | false
|
minerva
|
conv_115
|
uniq_conv_115
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{1}{s}-\\frac{1}{s} e^{-s T}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A signal \\(w(t)\\) is defined as\n\\[\nw(t)=u_{s}(t)-u_{s}(t-T)\n\\]\nwhere \\(T\\) is a fixed time in seconds and \\(u_{s}(t)\\) is the unit step. Compute the Laplace transform \\(W(s)\\) of \\(w(t)\\). Show your work.",
"role": "user"
}
],
"index": 115,
"single_turn_completion": "\\frac{1}{s}-\\frac{1}{s} e^{-s T}",
"single_turn_prompt": "A signal \\(w(t)\\) is defined as\n\\[\nw(t)=u_{s}(t)-u_{s}(t-T)\n\\]\nwhere \\(T\\) is a fixed time in seconds and \\(u_{s}(t)\\) is the unit step. Compute the Laplace transform \\(W(s)\\) of \\(w(t)\\). Show your work.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Assume that we apply a unit step in force separately to a mass \\(m\\), a dashpot \\(c\\), and a spring \\(k\\). The mass moves in inertial space. The spring and dashpot have one end connected to inertial space (reference velocity \\(=0\\) ), and the force is applied to the other end. Assume zero initial velocity and position for the elements.\nRecall that the unit step function \\(u_{S}(t)\\) is defined as \\(u_{S}(t)=0 ; t<0\\) and \\(u_{S}(t)=1 ; t \\geq 0\\). We will also find it useful to introduce the unit impulse function \\(\\delta(t)\\) which can be defined via\n\\[\nu_{S}(t)=\\int_{-\\infty}^{t} \\delta(\\tau) d \\tau\n\\]\nThis means that we can also view the unit impulse as the derivative of the unit step:\n\\[\n\\delta(t)=\\frac{d u_{S}(t)}{d t}\n\\]\n\nSolve for the resulting velocity of the mass.",
"role": "user"
}
] | false
|
minerva
|
conv_116
|
uniq_conv_116
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{1}{m} t",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Assume that we apply a unit step in force separately to a mass \\(m\\), a dashpot \\(c\\), and a spring \\(k\\). The mass moves in inertial space. The spring and dashpot have one end connected to inertial space (reference velocity \\(=0\\) ), and the force is applied to the other end. Assume zero initial velocity and position for the elements.\nRecall that the unit step function \\(u_{S}(t)\\) is defined as \\(u_{S}(t)=0 ; t<0\\) and \\(u_{S}(t)=1 ; t \\geq 0\\). We will also find it useful to introduce the unit impulse function \\(\\delta(t)\\) which can be defined via\n\\[\nu_{S}(t)=\\int_{-\\infty}^{t} \\delta(\\tau) d \\tau\n\\]\nThis means that we can also view the unit impulse as the derivative of the unit step:\n\\[\n\\delta(t)=\\frac{d u_{S}(t)}{d t}\n\\]\n\nSolve for the resulting velocity of the mass.",
"role": "user"
}
],
"index": 116,
"single_turn_completion": "\\frac{1}{m} t",
"single_turn_prompt": "Preamble: Assume that we apply a unit step in force separately to a mass \\(m\\), a dashpot \\(c\\), and a spring \\(k\\). The mass moves in inertial space. The spring and dashpot have one end connected to inertial space (reference velocity \\(=0\\) ), and the force is applied to the other end. Assume zero initial velocity and position for the elements.\nRecall that the unit step function \\(u_{S}(t)\\) is defined as \\(u_{S}(t)=0 ; t<0\\) and \\(u_{S}(t)=1 ; t \\geq 0\\). We will also find it useful to introduce the unit impulse function \\(\\delta(t)\\) which can be defined via\n\\[\nu_{S}(t)=\\int_{-\\infty}^{t} \\delta(\\tau) d \\tau\n\\]\nThis means that we can also view the unit impulse as the derivative of the unit step:\n\\[\n\\delta(t)=\\frac{d u_{S}(t)}{d t}\n\\]\n\nSolve for the resulting velocity of the mass.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For each of the functions $y(t)$, find the Laplace Transform $Y(s)$ :\n\nSubproblem 0: $y(t)=e^{-a t}$\n\n\nSolution: This function is one of the most widely used in dynamic systems, so we memorize its transform!\n\\[\nY(s)=\\boxed{\\frac{1}{s+a}}\n\\]\n\nFinal answer: The final answer is \\frac{1}{s+a}. I hope it is correct.\n\nSubproblem 1: $y(t)=e^{-\\sigma t} \\sin \\omega_{d} t$\n\n\nSolution: \\[\nY(s)=\\boxed{\\frac{\\omega_{d}}{(s+\\sigma)^{2}+\\omega_{d}^{2}}}\n\\]\n\nFinal answer: The final answer is \\frac{\\omega_{d}}{(s+\\sigma)^{2}+\\omega_{d}^{2}}. I hope it is correct.\n\nSubproblem 2: $y(t)=e^{-\\sigma t} \\cos \\omega_{d} t$",
"role": "user"
}
] | false
|
minerva
|
conv_117
|
uniq_conv_117
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{s+\\sigma}{(s+\\sigma)^{2}+\\omega_{d}^{2}}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For each of the functions $y(t)$, find the Laplace Transform $Y(s)$ :\n\nSubproblem 0: $y(t)=e^{-a t}$\n\n\nSolution: This function is one of the most widely used in dynamic systems, so we memorize its transform!\n\\[\nY(s)=\\boxed{\\frac{1}{s+a}}\n\\]\n\nFinal answer: The final answer is \\frac{1}{s+a}. I hope it is correct.\n\nSubproblem 1: $y(t)=e^{-\\sigma t} \\sin \\omega_{d} t$\n\n\nSolution: \\[\nY(s)=\\boxed{\\frac{\\omega_{d}}{(s+\\sigma)^{2}+\\omega_{d}^{2}}}\n\\]\n\nFinal answer: The final answer is \\frac{\\omega_{d}}{(s+\\sigma)^{2}+\\omega_{d}^{2}}. I hope it is correct.\n\nSubproblem 2: $y(t)=e^{-\\sigma t} \\cos \\omega_{d} t$",
"role": "user"
}
],
"index": 117,
"single_turn_completion": "\\frac{s+\\sigma}{(s+\\sigma)^{2}+\\omega_{d}^{2}}",
"single_turn_prompt": "Preamble: For each of the functions $y(t)$, find the Laplace Transform $Y(s)$ :\n\nSubproblem 0: $y(t)=e^{-a t}$\n\n\nSolution: This function is one of the most widely used in dynamic systems, so we memorize its transform!\n\\[\nY(s)=\\boxed{\\frac{1}{s+a}}\n\\]\n\nFinal answer: The final answer is \\frac{1}{s+a}. I hope it is correct.\n\nSubproblem 1: $y(t)=e^{-\\sigma t} \\sin \\omega_{d} t$\n\n\nSolution: \\[\nY(s)=\\boxed{\\frac{\\omega_{d}}{(s+\\sigma)^{2}+\\omega_{d}^{2}}}\n\\]\n\nFinal answer: The final answer is \\frac{\\omega_{d}}{(s+\\sigma)^{2}+\\omega_{d}^{2}}. I hope it is correct.\n\nSubproblem 2: $y(t)=e^{-\\sigma t} \\cos \\omega_{d} t$",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For each of the functions $y(t)$, find the Laplace Transform $Y(s)$ :\n\nSubproblem 0: $y(t)=e^{-a t}$\n\n\nSolution: This function is one of the most widely used in dynamic systems, so we memorize its transform!\n\\[\nY(s)=\\boxed{\\frac{1}{s+a}}\n\\]\n\nFinal answer: The final answer is \\frac{1}{s+a}. I hope it is correct.\n\nSubproblem 1: $y(t)=e^{-\\sigma t} \\sin \\omega_{d} t$",
"role": "user"
}
] | false
|
minerva
|
conv_118
|
uniq_conv_118
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{\\omega_{d}}{(s+\\sigma)^{2}+\\omega_{d}^{2}}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For each of the functions $y(t)$, find the Laplace Transform $Y(s)$ :\n\nSubproblem 0: $y(t)=e^{-a t}$\n\n\nSolution: This function is one of the most widely used in dynamic systems, so we memorize its transform!\n\\[\nY(s)=\\boxed{\\frac{1}{s+a}}\n\\]\n\nFinal answer: The final answer is \\frac{1}{s+a}. I hope it is correct.\n\nSubproblem 1: $y(t)=e^{-\\sigma t} \\sin \\omega_{d} t$",
"role": "user"
}
],
"index": 118,
"single_turn_completion": "\\frac{\\omega_{d}}{(s+\\sigma)^{2}+\\omega_{d}^{2}}",
"single_turn_prompt": "Preamble: For each of the functions $y(t)$, find the Laplace Transform $Y(s)$ :\n\nSubproblem 0: $y(t)=e^{-a t}$\n\n\nSolution: This function is one of the most widely used in dynamic systems, so we memorize its transform!\n\\[\nY(s)=\\boxed{\\frac{1}{s+a}}\n\\]\n\nFinal answer: The final answer is \\frac{1}{s+a}. I hope it is correct.\n\nSubproblem 1: $y(t)=e^{-\\sigma t} \\sin \\omega_{d} t$",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Consider the mass \\(m\\) sliding horizontally under the influence of the applied force \\(f\\) and a friction force which can be approximated by a linear friction element with coefficient \\(b\\). \n\nFormulate the state-determined equation of motion for the velocity \\(v\\) as output and the force \\(f\\) as input.",
"role": "user"
}
] | false
|
minerva
|
conv_119
|
uniq_conv_119
| 1
|
minerva
|
minerva
|
{
"ground_truth": "m \\frac{d v}{d t}+b v=f",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Consider the mass \\(m\\) sliding horizontally under the influence of the applied force \\(f\\) and a friction force which can be approximated by a linear friction element with coefficient \\(b\\). \n\nFormulate the state-determined equation of motion for the velocity \\(v\\) as output and the force \\(f\\) as input.",
"role": "user"
}
],
"index": 119,
"single_turn_completion": "m \\frac{d v}{d t}+b v=f",
"single_turn_prompt": "Preamble: Consider the mass \\(m\\) sliding horizontally under the influence of the applied force \\(f\\) and a friction force which can be approximated by a linear friction element with coefficient \\(b\\). \n\nFormulate the state-determined equation of motion for the velocity \\(v\\) as output and the force \\(f\\) as input.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Consider the rotor with moment of inertia \\(I\\) rotating under the influence of an applied torque \\(T\\) and the frictional torques from two bearings, each of which can be approximated by a linear frictional element with coefficient \\(B\\).\n\nSubproblem 0: Formulate the state-determined equation of motion for the angular velocity $\\omega$ as output and the torque $T$ as input.\n\n\nSolution: The equation of motion is\n\\[\n\\boxed{I \\frac{d \\omega}{d t}+2 B \\omega=T} \\quad \\text { or } \\quad \\frac{d \\omega}{d t}=-\\frac{2 B}{I} \\omega+\\frac{1}{I} T\n\\]\n\nFinal answer: The final answer is I \\frac{d \\omega}{d t}+2 B \\omega=T. I hope it is correct.\n\nSubproblem 1: Consider the case where:\n\\[\n\\begin{aligned}\nI &=0.001 \\mathrm{~kg}-\\mathrm{m}^{2} \\\\\nB &=0.005 \\mathrm{~N}-\\mathrm{m} / \\mathrm{r} / \\mathrm{s}\n\\end{aligned}\n\\]\nWhat is the steady-state velocity \\(\\omega_{s s}\\), in radians per second, when the input is a constant torque of 10 Newton-meters?",
"role": "user"
}
] | false
|
minerva
|
conv_120
|
uniq_conv_120
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1000",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Consider the rotor with moment of inertia \\(I\\) rotating under the influence of an applied torque \\(T\\) and the frictional torques from two bearings, each of which can be approximated by a linear frictional element with coefficient \\(B\\).\n\nSubproblem 0: Formulate the state-determined equation of motion for the angular velocity $\\omega$ as output and the torque $T$ as input.\n\n\nSolution: The equation of motion is\n\\[\n\\boxed{I \\frac{d \\omega}{d t}+2 B \\omega=T} \\quad \\text { or } \\quad \\frac{d \\omega}{d t}=-\\frac{2 B}{I} \\omega+\\frac{1}{I} T\n\\]\n\nFinal answer: The final answer is I \\frac{d \\omega}{d t}+2 B \\omega=T. I hope it is correct.\n\nSubproblem 1: Consider the case where:\n\\[\n\\begin{aligned}\nI &=0.001 \\mathrm{~kg}-\\mathrm{m}^{2} \\\\\nB &=0.005 \\mathrm{~N}-\\mathrm{m} / \\mathrm{r} / \\mathrm{s}\n\\end{aligned}\n\\]\nWhat is the steady-state velocity \\(\\omega_{s s}\\), in radians per second, when the input is a constant torque of 10 Newton-meters?",
"role": "user"
}
],
"index": 120,
"single_turn_completion": "1000",
"single_turn_prompt": "Preamble: Consider the rotor with moment of inertia \\(I\\) rotating under the influence of an applied torque \\(T\\) and the frictional torques from two bearings, each of which can be approximated by a linear frictional element with coefficient \\(B\\).\n\nSubproblem 0: Formulate the state-determined equation of motion for the angular velocity $\\omega$ as output and the torque $T$ as input.\n\n\nSolution: The equation of motion is\n\\[\n\\boxed{I \\frac{d \\omega}{d t}+2 B \\omega=T} \\quad \\text { or } \\quad \\frac{d \\omega}{d t}=-\\frac{2 B}{I} \\omega+\\frac{1}{I} T\n\\]\n\nFinal answer: The final answer is I \\frac{d \\omega}{d t}+2 B \\omega=T. I hope it is correct.\n\nSubproblem 1: Consider the case where:\n\\[\n\\begin{aligned}\nI &=0.001 \\mathrm{~kg}-\\mathrm{m}^{2} \\\\\nB &=0.005 \\mathrm{~N}-\\mathrm{m} / \\mathrm{r} / \\mathrm{s}\n\\end{aligned}\n\\]\nWhat is the steady-state velocity \\(\\omega_{s s}\\), in radians per second, when the input is a constant torque of 10 Newton-meters?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Consider the mass \\(m\\) sliding horizontally under the influence of the applied force \\(f\\) and a friction force which can be approximated by a linear friction element with coefficient \\(b\\). \n\nSubproblem 0: Formulate the state-determined equation of motion for the velocity \\(v\\) as output and the force \\(f\\) as input.\n\n\nSolution: The equation of motion is\n\\[\n\\boxed{m \\frac{d v}{d t}+b v=f} \\quad \\text { or } \\quad \\frac{d v}{d t}=-\\frac{b}{m} v+\\frac{1}{m} f\n\\]\n\nFinal answer: The final answer is m \\frac{d v}{d t}+b v=f. I hope it is correct.\n\nSubproblem 1: Consider the case where:\n\\[\n\\begin{aligned}\nm &=1000 \\mathrm{~kg} \\\\\nb &=100 \\mathrm{~N} / \\mathrm{m} / \\mathrm{s}\n\\end{aligned}\n\\]\nWhat is the steady-state velocity \\(v_{s s}\\) when the input is a constant force of 10 Newtons? Answer in meters per second.",
"role": "user"
}
] | false
|
minerva
|
conv_121
|
uniq_conv_121
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.10",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Consider the mass \\(m\\) sliding horizontally under the influence of the applied force \\(f\\) and a friction force which can be approximated by a linear friction element with coefficient \\(b\\). \n\nSubproblem 0: Formulate the state-determined equation of motion for the velocity \\(v\\) as output and the force \\(f\\) as input.\n\n\nSolution: The equation of motion is\n\\[\n\\boxed{m \\frac{d v}{d t}+b v=f} \\quad \\text { or } \\quad \\frac{d v}{d t}=-\\frac{b}{m} v+\\frac{1}{m} f\n\\]\n\nFinal answer: The final answer is m \\frac{d v}{d t}+b v=f. I hope it is correct.\n\nSubproblem 1: Consider the case where:\n\\[\n\\begin{aligned}\nm &=1000 \\mathrm{~kg} \\\\\nb &=100 \\mathrm{~N} / \\mathrm{m} / \\mathrm{s}\n\\end{aligned}\n\\]\nWhat is the steady-state velocity \\(v_{s s}\\) when the input is a constant force of 10 Newtons? Answer in meters per second.",
"role": "user"
}
],
"index": 121,
"single_turn_completion": "0.10",
"single_turn_prompt": "Preamble: Consider the mass \\(m\\) sliding horizontally under the influence of the applied force \\(f\\) and a friction force which can be approximated by a linear friction element with coefficient \\(b\\). \n\nSubproblem 0: Formulate the state-determined equation of motion for the velocity \\(v\\) as output and the force \\(f\\) as input.\n\n\nSolution: The equation of motion is\n\\[\n\\boxed{m \\frac{d v}{d t}+b v=f} \\quad \\text { or } \\quad \\frac{d v}{d t}=-\\frac{b}{m} v+\\frac{1}{m} f\n\\]\n\nFinal answer: The final answer is m \\frac{d v}{d t}+b v=f. I hope it is correct.\n\nSubproblem 1: Consider the case where:\n\\[\n\\begin{aligned}\nm &=1000 \\mathrm{~kg} \\\\\nb &=100 \\mathrm{~N} / \\mathrm{m} / \\mathrm{s}\n\\end{aligned}\n\\]\nWhat is the steady-state velocity \\(v_{s s}\\) when the input is a constant force of 10 Newtons? Answer in meters per second.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Obtain the inverse Laplace transform of the following frequency-domain expression: $F(s) = -\\frac{(4 s-10)}{s(s+2)(s+5)}$.\nUse $u(t)$ to denote the unit step function.",
"role": "user"
}
] | false
|
minerva
|
conv_122
|
uniq_conv_122
| 1
|
minerva
|
minerva
|
{
"ground_truth": "(1 - 3e^{-2t} + 2e^{-5t}) u(t)",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Obtain the inverse Laplace transform of the following frequency-domain expression: $F(s) = -\\frac{(4 s-10)}{s(s+2)(s+5)}$.\nUse $u(t)$ to denote the unit step function.",
"role": "user"
}
],
"index": 122,
"single_turn_completion": "(1 - 3e^{-2t} + 2e^{-5t}) u(t)",
"single_turn_prompt": "Obtain the inverse Laplace transform of the following frequency-domain expression: $F(s) = -\\frac{(4 s-10)}{s(s+2)(s+5)}$.\nUse $u(t)$ to denote the unit step function.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: A signal has a Laplace transform\n\\[\nX(s)=b+\\frac{a}{s(s+a)}\n\\]\nwhere \\(a, b>0\\), and with a region of convergence of \\(|s|>0\\). Find \\(x(t), t>0\\).",
"role": "user"
}
] | false
|
minerva
|
conv_123
|
uniq_conv_123
| 1
|
minerva
|
minerva
|
{
"ground_truth": "b \\delta(t)+1-e^{-a t}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A signal has a Laplace transform\n\\[\nX(s)=b+\\frac{a}{s(s+a)}\n\\]\nwhere \\(a, b>0\\), and with a region of convergence of \\(|s|>0\\). Find \\(x(t), t>0\\).",
"role": "user"
}
],
"index": 123,
"single_turn_completion": "b \\delta(t)+1-e^{-a t}",
"single_turn_prompt": "A signal has a Laplace transform\n\\[\nX(s)=b+\\frac{a}{s(s+a)}\n\\]\nwhere \\(a, b>0\\), and with a region of convergence of \\(|s|>0\\). Find \\(x(t), t>0\\).",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For each Laplace Transform \\(Y(s)\\), find the function \\(y(t)\\) :\n\n\\[\nY(s)=\\boxed{\\frac{1}{(s+a)(s+b)}}\n\\]",
"role": "user"
}
] | false
|
minerva
|
conv_124
|
uniq_conv_124
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{1}{b-a}\\left(e^{-a t}-e^{-b t}\\right)",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For each Laplace Transform \\(Y(s)\\), find the function \\(y(t)\\) :\n\n\\[\nY(s)=\\boxed{\\frac{1}{(s+a)(s+b)}}\n\\]",
"role": "user"
}
],
"index": 124,
"single_turn_completion": "\\frac{1}{b-a}\\left(e^{-a t}-e^{-b t}\\right)",
"single_turn_prompt": "Preamble: For each Laplace Transform \\(Y(s)\\), find the function \\(y(t)\\) :\n\n\\[\nY(s)=\\boxed{\\frac{1}{(s+a)(s+b)}}\n\\]",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Consider the rotor with moment of inertia \\(I\\) rotating under the influence of an applied torque \\(T\\) and the frictional torques from two bearings, each of which can be approximated by a linear frictional element with coefficient \\(B\\).\n\nFormulate the state-determined equation of motion for the angular velocity $\\omega$ as output and the torque $T$ as input.",
"role": "user"
}
] | false
|
minerva
|
conv_125
|
uniq_conv_125
| 1
|
minerva
|
minerva
|
{
"ground_truth": "I \\frac{d \\omega}{d t}+2 B \\omega=T",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Consider the rotor with moment of inertia \\(I\\) rotating under the influence of an applied torque \\(T\\) and the frictional torques from two bearings, each of which can be approximated by a linear frictional element with coefficient \\(B\\).\n\nFormulate the state-determined equation of motion for the angular velocity $\\omega$ as output and the torque $T$ as input.",
"role": "user"
}
],
"index": 125,
"single_turn_completion": "I \\frac{d \\omega}{d t}+2 B \\omega=T",
"single_turn_prompt": "Preamble: Consider the rotor with moment of inertia \\(I\\) rotating under the influence of an applied torque \\(T\\) and the frictional torques from two bearings, each of which can be approximated by a linear frictional element with coefficient \\(B\\).\n\nFormulate the state-determined equation of motion for the angular velocity $\\omega$ as output and the torque $T$ as input.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Obtain the inverse Laplace transform of the following frequency-domain expression: $F(s) = \\frac{4}{s^2(s^2+4)}$.\nUse $u(t)$ to denote the unit step function.",
"role": "user"
}
] | false
|
minerva
|
conv_126
|
uniq_conv_126
| 1
|
minerva
|
minerva
|
{
"ground_truth": "(t + \\frac{1}{2} \\sin{2t}) u(t)",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Obtain the inverse Laplace transform of the following frequency-domain expression: $F(s) = \\frac{4}{s^2(s^2+4)}$.\nUse $u(t)$ to denote the unit step function.",
"role": "user"
}
],
"index": 126,
"single_turn_completion": "(t + \\frac{1}{2} \\sin{2t}) u(t)",
"single_turn_prompt": "Obtain the inverse Laplace transform of the following frequency-domain expression: $F(s) = \\frac{4}{s^2(s^2+4)}$.\nUse $u(t)$ to denote the unit step function.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: This problem considers the simple RLC circuit, in which a voltage source $v_{i}$ is in series with a resistor $R$, inductor $L$, and capacitor $C$. We measure the voltage $v_{o}$ across the capacitor. $v_{i}$ and $v_{o}$ share a ground reference.\n\nCalculate the transfer function \\(V_{o}(s) / V_{i}(s)\\).",
"role": "user"
}
] | false
|
minerva
|
conv_127
|
uniq_conv_127
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{1}{L C s^{2}+R C s+1}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: This problem considers the simple RLC circuit, in which a voltage source $v_{i}$ is in series with a resistor $R$, inductor $L$, and capacitor $C$. We measure the voltage $v_{o}$ across the capacitor. $v_{i}$ and $v_{o}$ share a ground reference.\n\nCalculate the transfer function \\(V_{o}(s) / V_{i}(s)\\).",
"role": "user"
}
],
"index": 127,
"single_turn_completion": "\\frac{1}{L C s^{2}+R C s+1}",
"single_turn_prompt": "Preamble: This problem considers the simple RLC circuit, in which a voltage source $v_{i}$ is in series with a resistor $R$, inductor $L$, and capacitor $C$. We measure the voltage $v_{o}$ across the capacitor. $v_{i}$ and $v_{o}$ share a ground reference.\n\nCalculate the transfer function \\(V_{o}(s) / V_{i}(s)\\).",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: You are given an equation of motion of the form:\n\\[\n\\dot{y}+5 y=10 u\n\\]\n\nWhat is the time constant for this system?",
"role": "user"
}
] | false
|
minerva
|
conv_128
|
uniq_conv_128
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.2",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: You are given an equation of motion of the form:\n\\[\n\\dot{y}+5 y=10 u\n\\]\n\nWhat is the time constant for this system?",
"role": "user"
}
],
"index": 128,
"single_turn_completion": "0.2",
"single_turn_prompt": "Preamble: You are given an equation of motion of the form:\n\\[\n\\dot{y}+5 y=10 u\n\\]\n\nWhat is the time constant for this system?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: This problem considers the simple RLC circuit, in which a voltage source $v_{i}$ is in series with a resistor $R$, inductor $L$, and capacitor $C$. We measure the voltage $v_{o}$ across the capacitor. $v_{i}$ and $v_{o}$ share a ground reference.\n\nSubproblem 0: Calculate the transfer function \\(V_{o}(s) / V_{i}(s)\\).\n\n\nSolution: Using the voltage divider relationship:\n\\[\n\\begin{aligned}\nV_{o}(s) &=\\frac{Z_{e q}}{Z_{\\text {total }}}V_{i}(s)=\\frac{\\frac{1}{C s}}{R+L s+\\frac{1}{C s}} V_{i}(s) \\\\\n\\frac{V_{o}(s)}{V_{i}(s)} &=\\boxed{\\frac{1}{L C s^{2}+R C s+1}}\n\\end{aligned}\n\\]\n\nFinal answer: The final answer is \\frac{1}{L C s^{2}+R C s+1}. I hope it is correct.\n\nSubproblem 1: Let \\(L=0.01 \\mathrm{H}\\). Choose the value of $C$ such that \\(\\omega_{n}=10^{5}\\) and \\(\\zeta=0.05\\). Give your answer in Farads.",
"role": "user"
}
] | false
|
minerva
|
conv_129
|
uniq_conv_129
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1e-8",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: This problem considers the simple RLC circuit, in which a voltage source $v_{i}$ is in series with a resistor $R$, inductor $L$, and capacitor $C$. We measure the voltage $v_{o}$ across the capacitor. $v_{i}$ and $v_{o}$ share a ground reference.\n\nSubproblem 0: Calculate the transfer function \\(V_{o}(s) / V_{i}(s)\\).\n\n\nSolution: Using the voltage divider relationship:\n\\[\n\\begin{aligned}\nV_{o}(s) &=\\frac{Z_{e q}}{Z_{\\text {total }}}V_{i}(s)=\\frac{\\frac{1}{C s}}{R+L s+\\frac{1}{C s}} V_{i}(s) \\\\\n\\frac{V_{o}(s)}{V_{i}(s)} &=\\boxed{\\frac{1}{L C s^{2}+R C s+1}}\n\\end{aligned}\n\\]\n\nFinal answer: The final answer is \\frac{1}{L C s^{2}+R C s+1}. I hope it is correct.\n\nSubproblem 1: Let \\(L=0.01 \\mathrm{H}\\). Choose the value of $C$ such that \\(\\omega_{n}=10^{5}\\) and \\(\\zeta=0.05\\). Give your answer in Farads.",
"role": "user"
}
],
"index": 129,
"single_turn_completion": "1e-8",
"single_turn_prompt": "Preamble: This problem considers the simple RLC circuit, in which a voltage source $v_{i}$ is in series with a resistor $R$, inductor $L$, and capacitor $C$. We measure the voltage $v_{o}$ across the capacitor. $v_{i}$ and $v_{o}$ share a ground reference.\n\nSubproblem 0: Calculate the transfer function \\(V_{o}(s) / V_{i}(s)\\).\n\n\nSolution: Using the voltage divider relationship:\n\\[\n\\begin{aligned}\nV_{o}(s) &=\\frac{Z_{e q}}{Z_{\\text {total }}}V_{i}(s)=\\frac{\\frac{1}{C s}}{R+L s+\\frac{1}{C s}} V_{i}(s) \\\\\n\\frac{V_{o}(s)}{V_{i}(s)} &=\\boxed{\\frac{1}{L C s^{2}+R C s+1}}\n\\end{aligned}\n\\]\n\nFinal answer: The final answer is \\frac{1}{L C s^{2}+R C s+1}. I hope it is correct.\n\nSubproblem 1: Let \\(L=0.01 \\mathrm{H}\\). Choose the value of $C$ such that \\(\\omega_{n}=10^{5}\\) and \\(\\zeta=0.05\\). Give your answer in Farads.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Here we consider a system described by the differential equation\n\\[\n\\ddot{y}+10 \\dot{y}+10000 y=0 .\n\\]\n\nWhat is the value of the natural frequency \\(\\omega_{n}\\) in radians per second?",
"role": "user"
}
] | false
|
minerva
|
conv_130
|
uniq_conv_130
| 1
|
minerva
|
minerva
|
{
"ground_truth": "100",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Here we consider a system described by the differential equation\n\\[\n\\ddot{y}+10 \\dot{y}+10000 y=0 .\n\\]\n\nWhat is the value of the natural frequency \\(\\omega_{n}\\) in radians per second?",
"role": "user"
}
],
"index": 130,
"single_turn_completion": "100",
"single_turn_prompt": "Preamble: Here we consider a system described by the differential equation\n\\[\n\\ddot{y}+10 \\dot{y}+10000 y=0 .\n\\]\n\nWhat is the value of the natural frequency \\(\\omega_{n}\\) in radians per second?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Consider a circuit in which a voltage source of voltage in $v_{i}(t)$ is connected in series with an inductor $L$ and capacitor $C$. We consider the voltage across the capacitor $v_{o}(t)$ to be the output of the system.\nBoth $v_{i}(t)$ and $v_{o}(t)$ share ground reference.\n\nWrite the governing differential equation for this circuit.",
"role": "user"
}
] | false
|
minerva
|
conv_131
|
uniq_conv_131
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{d^{2} v_{o}}{d t^{2}}+\\frac{v_{o}}{L C}=\\frac{v_{i}}{L C}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Consider a circuit in which a voltage source of voltage in $v_{i}(t)$ is connected in series with an inductor $L$ and capacitor $C$. We consider the voltage across the capacitor $v_{o}(t)$ to be the output of the system.\nBoth $v_{i}(t)$ and $v_{o}(t)$ share ground reference.\n\nWrite the governing differential equation for this circuit.",
"role": "user"
}
],
"index": 131,
"single_turn_completion": "\\frac{d^{2} v_{o}}{d t^{2}}+\\frac{v_{o}}{L C}=\\frac{v_{i}}{L C}",
"single_turn_prompt": "Preamble: Consider a circuit in which a voltage source of voltage in $v_{i}(t)$ is connected in series with an inductor $L$ and capacitor $C$. We consider the voltage across the capacitor $v_{o}(t)$ to be the output of the system.\nBoth $v_{i}(t)$ and $v_{o}(t)$ share ground reference.\n\nWrite the governing differential equation for this circuit.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Write (but don't solve) the equation of motion for a pendulum consisting of a mass $m$ attached to a rigid massless rod, suspended from the ceiling and free to rotate in a single vertical plane. Let the rod (of length $l$) make an angle of $\\theta$ with the vertical. Gravity ($mg$) acts directly downward, the system input is a horizontal external force $f(t)$, and the system output is the angle $\\theta(t)$. \nNote: Do NOT make the small-angle approximation in your equation.",
"role": "user"
}
] | false
|
minerva
|
conv_132
|
uniq_conv_132
| 1
|
minerva
|
minerva
|
{
"ground_truth": "m l \\ddot{\\theta}(t)-m g \\sin \\theta(t)=f(t) \\cos \\theta(t)",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Write (but don't solve) the equation of motion for a pendulum consisting of a mass $m$ attached to a rigid massless rod, suspended from the ceiling and free to rotate in a single vertical plane. Let the rod (of length $l$) make an angle of $\\theta$ with the vertical. Gravity ($mg$) acts directly downward, the system input is a horizontal external force $f(t)$, and the system output is the angle $\\theta(t)$. \nNote: Do NOT make the small-angle approximation in your equation.",
"role": "user"
}
],
"index": 132,
"single_turn_completion": "m l \\ddot{\\theta}(t)-m g \\sin \\theta(t)=f(t) \\cos \\theta(t)",
"single_turn_prompt": "Write (but don't solve) the equation of motion for a pendulum consisting of a mass $m$ attached to a rigid massless rod, suspended from the ceiling and free to rotate in a single vertical plane. Let the rod (of length $l$) make an angle of $\\theta$ with the vertical. Gravity ($mg$) acts directly downward, the system input is a horizontal external force $f(t)$, and the system output is the angle $\\theta(t)$. \nNote: Do NOT make the small-angle approximation in your equation.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Here we consider a system described by the differential equation\n\\[\n\\ddot{y}+10 \\dot{y}+10000 y=0 .\n\\]\n\nSubproblem 0: What is the value of the natural frequency \\(\\omega_{n}\\) in radians per second?\n\n\nSolution: $\\omega_{n}=\\sqrt{\\frac{k}{m}}$\nSo\n$\\omega_{n} =\\boxed{100} \\mathrm{rad} / \\mathrm{s}$\n\nFinal answer: The final answer is 100. I hope it is correct.\n\nSubproblem 1: What is the value of the damping ratio \\(\\zeta\\)? \n\n\nSolution: $\\zeta=\\frac{b}{2 \\sqrt{k m}}$\nSo\n$\\zeta =\\boxed{0.05}$\n\nFinal answer: The final answer is 0.05. I hope it is correct.\n\nSubproblem 2: What is the value of the damped natural frequency \\(\\omega_{d}\\) in radians per second? Give your answer to three significant figures.",
"role": "user"
}
] | false
|
minerva
|
conv_133
|
uniq_conv_133
| 1
|
minerva
|
minerva
|
{
"ground_truth": "99.9",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Here we consider a system described by the differential equation\n\\[\n\\ddot{y}+10 \\dot{y}+10000 y=0 .\n\\]\n\nSubproblem 0: What is the value of the natural frequency \\(\\omega_{n}\\) in radians per second?\n\n\nSolution: $\\omega_{n}=\\sqrt{\\frac{k}{m}}$\nSo\n$\\omega_{n} =\\boxed{100} \\mathrm{rad} / \\mathrm{s}$\n\nFinal answer: The final answer is 100. I hope it is correct.\n\nSubproblem 1: What is the value of the damping ratio \\(\\zeta\\)? \n\n\nSolution: $\\zeta=\\frac{b}{2 \\sqrt{k m}}$\nSo\n$\\zeta =\\boxed{0.05}$\n\nFinal answer: The final answer is 0.05. I hope it is correct.\n\nSubproblem 2: What is the value of the damped natural frequency \\(\\omega_{d}\\) in radians per second? Give your answer to three significant figures.",
"role": "user"
}
],
"index": 133,
"single_turn_completion": "99.9",
"single_turn_prompt": "Preamble: Here we consider a system described by the differential equation\n\\[\n\\ddot{y}+10 \\dot{y}+10000 y=0 .\n\\]\n\nSubproblem 0: What is the value of the natural frequency \\(\\omega_{n}\\) in radians per second?\n\n\nSolution: $\\omega_{n}=\\sqrt{\\frac{k}{m}}$\nSo\n$\\omega_{n} =\\boxed{100} \\mathrm{rad} / \\mathrm{s}$\n\nFinal answer: The final answer is 100. I hope it is correct.\n\nSubproblem 1: What is the value of the damping ratio \\(\\zeta\\)? \n\n\nSolution: $\\zeta=\\frac{b}{2 \\sqrt{k m}}$\nSo\n$\\zeta =\\boxed{0.05}$\n\nFinal answer: The final answer is 0.05. I hope it is correct.\n\nSubproblem 2: What is the value of the damped natural frequency \\(\\omega_{d}\\) in radians per second? Give your answer to three significant figures.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Here we consider a system described by the differential equation\n\\[\n\\ddot{y}+10 \\dot{y}+10000 y=0 .\n\\]\n\nSubproblem 0: What is the value of the natural frequency \\(\\omega_{n}\\) in radians per second?\n\n\nSolution: $\\omega_{n}=\\sqrt{\\frac{k}{m}}$\nSo\n$\\omega_{n} =\\boxed{100} \\mathrm{rad} / \\mathrm{s}$\n\nFinal answer: The final answer is 100. I hope it is correct.\n\nSubproblem 1: What is the value of the damping ratio \\(\\zeta\\)?",
"role": "user"
}
] | false
|
minerva
|
conv_134
|
uniq_conv_134
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.05",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Here we consider a system described by the differential equation\n\\[\n\\ddot{y}+10 \\dot{y}+10000 y=0 .\n\\]\n\nSubproblem 0: What is the value of the natural frequency \\(\\omega_{n}\\) in radians per second?\n\n\nSolution: $\\omega_{n}=\\sqrt{\\frac{k}{m}}$\nSo\n$\\omega_{n} =\\boxed{100} \\mathrm{rad} / \\mathrm{s}$\n\nFinal answer: The final answer is 100. I hope it is correct.\n\nSubproblem 1: What is the value of the damping ratio \\(\\zeta\\)?",
"role": "user"
}
],
"index": 134,
"single_turn_completion": "0.05",
"single_turn_prompt": "Preamble: Here we consider a system described by the differential equation\n\\[\n\\ddot{y}+10 \\dot{y}+10000 y=0 .\n\\]\n\nSubproblem 0: What is the value of the natural frequency \\(\\omega_{n}\\) in radians per second?\n\n\nSolution: $\\omega_{n}=\\sqrt{\\frac{k}{m}}$\nSo\n$\\omega_{n} =\\boxed{100} \\mathrm{rad} / \\mathrm{s}$\n\nFinal answer: The final answer is 100. I hope it is correct.\n\nSubproblem 1: What is the value of the damping ratio \\(\\zeta\\)?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: What is the speed of light in meters/second to 1 significant figure? Use the format $a \\times 10^{b}$ where a and b are numbers.",
"role": "user"
}
] | false
|
minerva
|
conv_135
|
uniq_conv_135
| 1
|
minerva
|
minerva
|
{
"ground_truth": "3e8",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "What is the speed of light in meters/second to 1 significant figure? Use the format $a \\times 10^{b}$ where a and b are numbers.",
"role": "user"
}
],
"index": 135,
"single_turn_completion": "3e8",
"single_turn_prompt": "What is the speed of light in meters/second to 1 significant figure? Use the format $a \\times 10^{b}$ where a and b are numbers.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:\n\n\nSolution: \\boxed{400000} years.\n\nFinal answer: The final answer is 400000. I hope it is correct.\n\nSubproblem 2: Age of our universe today in Gyr:\n\n\nSolution: \\boxed{10} Gyr.\n\nFinal answer: The final answer is 10. I hope it is correct.\n\nSubproblem 3: Number of stars in our Galaxy: (Please format your answer as 'xen' representing $x * 10^n$)",
"role": "user"
}
] | false
|
minerva
|
conv_136
|
uniq_conv_136
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1e11",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:\n\n\nSolution: \\boxed{400000} years.\n\nFinal answer: The final answer is 400000. I hope it is correct.\n\nSubproblem 2: Age of our universe today in Gyr:\n\n\nSolution: \\boxed{10} Gyr.\n\nFinal answer: The final answer is 10. I hope it is correct.\n\nSubproblem 3: Number of stars in our Galaxy: (Please format your answer as 'xen' representing $x * 10^n$)",
"role": "user"
}
],
"index": 136,
"single_turn_completion": "1e11",
"single_turn_prompt": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:\n\n\nSolution: \\boxed{400000} years.\n\nFinal answer: The final answer is 400000. I hope it is correct.\n\nSubproblem 2: Age of our universe today in Gyr:\n\n\nSolution: \\boxed{10} Gyr.\n\nFinal answer: The final answer is 10. I hope it is correct.\n\nSubproblem 3: Number of stars in our Galaxy: (Please format your answer as 'xen' representing $x * 10^n$)",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: In a parallel universe, the Boston baseball team made the playoffs.\n\nManny Relativirez hits the ball and starts running towards first base at speed $\\beta$. How fast is he running, given that he sees third base $45^{\\circ}$ to his left (as opposed to straight to his left before he started running)? Assume that he is still very close to home plate. Give your answer in terms of the speed of light, $c$.",
"role": "user"
}
] | false
|
minerva
|
conv_137
|
uniq_conv_137
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{1}{\\sqrt{2}}c",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: In a parallel universe, the Boston baseball team made the playoffs.\n\nManny Relativirez hits the ball and starts running towards first base at speed $\\beta$. How fast is he running, given that he sees third base $45^{\\circ}$ to his left (as opposed to straight to his left before he started running)? Assume that he is still very close to home plate. Give your answer in terms of the speed of light, $c$.",
"role": "user"
}
],
"index": 137,
"single_turn_completion": "\\frac{1}{\\sqrt{2}}c",
"single_turn_prompt": "Preamble: In a parallel universe, the Boston baseball team made the playoffs.\n\nManny Relativirez hits the ball and starts running towards first base at speed $\\beta$. How fast is he running, given that he sees third base $45^{\\circ}$ to his left (as opposed to straight to his left before he started running)? Assume that he is still very close to home plate. Give your answer in terms of the speed of light, $c$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: In the Sun, one of the processes in the He fusion chain is $p+p+e^{-} \\rightarrow d+\\nu$, where $d$ is a deuteron. Make the approximations that the deuteron rest mass is $2 m_{p}$, and that $m_{e} \\approx 0$ and $m_{\\nu} \\approx 0$, since both the electron and the neutrino have negligible rest mass compared with the proton rest mass $m_{p}$.\n\nIn the lab frame, the two protons have the same energy $\\gamma m_{p}$ and impact angle $\\theta$, and the electron is at rest. Calculate the energy $E_{\\nu}$ of the neutrino in the rest frame of the deuteron in terms of $\\theta, m_{p}$ and $\\gamma$.",
"role": "user"
}
] | false
|
minerva
|
conv_138
|
uniq_conv_138
| 1
|
minerva
|
minerva
|
{
"ground_truth": "m_{p} c^{2}\\left(\\gamma^{2}-1\\right) \\sin ^{2} \\theta",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: In the Sun, one of the processes in the He fusion chain is $p+p+e^{-} \\rightarrow d+\\nu$, where $d$ is a deuteron. Make the approximations that the deuteron rest mass is $2 m_{p}$, and that $m_{e} \\approx 0$ and $m_{\\nu} \\approx 0$, since both the electron and the neutrino have negligible rest mass compared with the proton rest mass $m_{p}$.\n\nIn the lab frame, the two protons have the same energy $\\gamma m_{p}$ and impact angle $\\theta$, and the electron is at rest. Calculate the energy $E_{\\nu}$ of the neutrino in the rest frame of the deuteron in terms of $\\theta, m_{p}$ and $\\gamma$.",
"role": "user"
}
],
"index": 138,
"single_turn_completion": "m_{p} c^{2}\\left(\\gamma^{2}-1\\right) \\sin ^{2} \\theta",
"single_turn_prompt": "Preamble: In the Sun, one of the processes in the He fusion chain is $p+p+e^{-} \\rightarrow d+\\nu$, where $d$ is a deuteron. Make the approximations that the deuteron rest mass is $2 m_{p}$, and that $m_{e} \\approx 0$ and $m_{\\nu} \\approx 0$, since both the electron and the neutrino have negligible rest mass compared with the proton rest mass $m_{p}$.\n\nIn the lab frame, the two protons have the same energy $\\gamma m_{p}$ and impact angle $\\theta$, and the electron is at rest. Calculate the energy $E_{\\nu}$ of the neutrino in the rest frame of the deuteron in terms of $\\theta, m_{p}$ and $\\gamma$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: In a parallel universe, the Boston baseball team made the playoffs.\n\nSubproblem 0: Manny Relativirez hits the ball and starts running towards first base at speed $\\beta$. How fast is he running, given that he sees third base $45^{\\circ}$ to his left (as opposed to straight to his left before he started running)? Assume that he is still very close to home plate. Give your answer in terms of the speed of light, $c$.\n\n\nSolution: Using the aberration formula with $\\cos \\theta^{\\prime}=-1 / \\sqrt{2}, \\beta=1 / \\sqrt{2}$, so $v=\\boxed{\\frac{1}{\\sqrt{2}}c}$.\n\nFinal answer: The final answer is \\frac{1}{\\sqrt{2}}c. I hope it is correct.\n\nSubproblem 1: A player standing on third base is wearing red socks emitting light of wavelength $\\lambda_{\\text {red}}$. What wavelength does Manny see in terms of $\\lambda_{\\text {red}}$?",
"role": "user"
}
] | false
|
minerva
|
conv_139
|
uniq_conv_139
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\lambda_{\\text {red}} / \\sqrt{2}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: In a parallel universe, the Boston baseball team made the playoffs.\n\nSubproblem 0: Manny Relativirez hits the ball and starts running towards first base at speed $\\beta$. How fast is he running, given that he sees third base $45^{\\circ}$ to his left (as opposed to straight to his left before he started running)? Assume that he is still very close to home plate. Give your answer in terms of the speed of light, $c$.\n\n\nSolution: Using the aberration formula with $\\cos \\theta^{\\prime}=-1 / \\sqrt{2}, \\beta=1 / \\sqrt{2}$, so $v=\\boxed{\\frac{1}{\\sqrt{2}}c}$.\n\nFinal answer: The final answer is \\frac{1}{\\sqrt{2}}c. I hope it is correct.\n\nSubproblem 1: A player standing on third base is wearing red socks emitting light of wavelength $\\lambda_{\\text {red}}$. What wavelength does Manny see in terms of $\\lambda_{\\text {red}}$?",
"role": "user"
}
],
"index": 139,
"single_turn_completion": "\\lambda_{\\text {red}} / \\sqrt{2}",
"single_turn_prompt": "Preamble: In a parallel universe, the Boston baseball team made the playoffs.\n\nSubproblem 0: Manny Relativirez hits the ball and starts running towards first base at speed $\\beta$. How fast is he running, given that he sees third base $45^{\\circ}$ to his left (as opposed to straight to his left before he started running)? Assume that he is still very close to home plate. Give your answer in terms of the speed of light, $c$.\n\n\nSolution: Using the aberration formula with $\\cos \\theta^{\\prime}=-1 / \\sqrt{2}, \\beta=1 / \\sqrt{2}$, so $v=\\boxed{\\frac{1}{\\sqrt{2}}c}$.\n\nFinal answer: The final answer is \\frac{1}{\\sqrt{2}}c. I hope it is correct.\n\nSubproblem 1: A player standing on third base is wearing red socks emitting light of wavelength $\\lambda_{\\text {red}}$. What wavelength does Manny see in terms of $\\lambda_{\\text {red}}$?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:\n\n\nSolution: \\boxed{400000} years.\n\nFinal answer: The final answer is 400000. I hope it is correct.\n\nSubproblem 2: Age of our universe today in Gyr:",
"role": "user"
}
] | false
|
minerva
|
conv_140
|
uniq_conv_140
| 1
|
minerva
|
minerva
|
{
"ground_truth": "10",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:\n\n\nSolution: \\boxed{400000} years.\n\nFinal answer: The final answer is 400000. I hope it is correct.\n\nSubproblem 2: Age of our universe today in Gyr:",
"role": "user"
}
],
"index": 140,
"single_turn_completion": "10",
"single_turn_prompt": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:\n\n\nSolution: \\boxed{400000} years.\n\nFinal answer: The final answer is 400000. I hope it is correct.\n\nSubproblem 2: Age of our universe today in Gyr:",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: How many down quarks does a tritium ($H^3$) nucleus contain?",
"role": "user"
}
] | false
|
minerva
|
conv_141
|
uniq_conv_141
| 1
|
minerva
|
minerva
|
{
"ground_truth": "5",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "How many down quarks does a tritium ($H^3$) nucleus contain?",
"role": "user"
}
],
"index": 141,
"single_turn_completion": "5",
"single_turn_prompt": "How many down quarks does a tritium ($H^3$) nucleus contain?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: How many up quarks does a tritium ($H^3$) nucleus contain?",
"role": "user"
}
] | false
|
minerva
|
conv_142
|
uniq_conv_142
| 1
|
minerva
|
minerva
|
{
"ground_truth": "4",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "How many up quarks does a tritium ($H^3$) nucleus contain?",
"role": "user"
}
],
"index": 142,
"single_turn_completion": "4",
"single_turn_prompt": "How many up quarks does a tritium ($H^3$) nucleus contain?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nAge of our universe when most He nuclei were formed in minutes:",
"role": "user"
}
] | false
|
minerva
|
conv_143
|
uniq_conv_143
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nAge of our universe when most He nuclei were formed in minutes:",
"role": "user"
}
],
"index": 143,
"single_turn_completion": "1",
"single_turn_prompt": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nAge of our universe when most He nuclei were formed in minutes:",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:\n\n\nSolution: \\boxed{400000} years.\n\nFinal answer: The final answer is 400000. I hope it is correct.\n\nSubproblem 2: Age of our universe today in Gyr:\n\n\nSolution: \\boxed{10} Gyr.\n\nFinal answer: The final answer is 10. I hope it is correct.\n\nSubproblem 3: Number of stars in our Galaxy: (Please format your answer as 'xen' representing $x * 10^n$)\n\n\nSolution: \\boxed{1e11}.\n\nFinal answer: The final answer is 1e11. I hope it is correct.\n\nSubproblem 4: Light travel time to closest star (Sun!:) in minutes. (Please format your answer as an integer.)",
"role": "user"
}
] | false
|
minerva
|
conv_144
|
uniq_conv_144
| 1
|
minerva
|
minerva
|
{
"ground_truth": "8",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:\n\n\nSolution: \\boxed{400000} years.\n\nFinal answer: The final answer is 400000. I hope it is correct.\n\nSubproblem 2: Age of our universe today in Gyr:\n\n\nSolution: \\boxed{10} Gyr.\n\nFinal answer: The final answer is 10. I hope it is correct.\n\nSubproblem 3: Number of stars in our Galaxy: (Please format your answer as 'xen' representing $x * 10^n$)\n\n\nSolution: \\boxed{1e11}.\n\nFinal answer: The final answer is 1e11. I hope it is correct.\n\nSubproblem 4: Light travel time to closest star (Sun!:) in minutes. (Please format your answer as an integer.)",
"role": "user"
}
],
"index": 144,
"single_turn_completion": "8",
"single_turn_prompt": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:\n\n\nSolution: \\boxed{400000} years.\n\nFinal answer: The final answer is 400000. I hope it is correct.\n\nSubproblem 2: Age of our universe today in Gyr:\n\n\nSolution: \\boxed{10} Gyr.\n\nFinal answer: The final answer is 10. I hope it is correct.\n\nSubproblem 3: Number of stars in our Galaxy: (Please format your answer as 'xen' representing $x * 10^n$)\n\n\nSolution: \\boxed{1e11}.\n\nFinal answer: The final answer is 1e11. I hope it is correct.\n\nSubproblem 4: Light travel time to closest star (Sun!:) in minutes. (Please format your answer as an integer.)",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:",
"role": "user"
}
] | false
|
minerva
|
conv_145
|
uniq_conv_145
| 1
|
minerva
|
minerva
|
{
"ground_truth": "400000",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:",
"role": "user"
}
],
"index": 145,
"single_turn_completion": "400000",
"single_turn_prompt": "Preamble: Give each of the following quantities to the nearest power of 10 and in the units requested. \n\nSubproblem 0: Age of our universe when most He nuclei were formed in minutes: \n\n\nSolution: \\boxed{1} minute.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: Age of our universe when hydrogen atoms formed in years:",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Potassium metal can be used as the active surface in a photodiode because electrons are relatively easily removed from a potassium surface. The energy needed is $2.15 \\times 10^{5} J$ per mole of electrons removed ( 1 mole $=6.02 \\times 10^{23}$ electrons). What is the longest wavelength light (in nm) with quanta of sufficient energy to eject electrons from a potassium photodiode surface?",
"role": "user"
}
] | false
|
minerva
|
conv_146
|
uniq_conv_146
| 1
|
minerva
|
minerva
|
{
"ground_truth": "560",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Potassium metal can be used as the active surface in a photodiode because electrons are relatively easily removed from a potassium surface. The energy needed is $2.15 \\times 10^{5} J$ per mole of electrons removed ( 1 mole $=6.02 \\times 10^{23}$ electrons). What is the longest wavelength light (in nm) with quanta of sufficient energy to eject electrons from a potassium photodiode surface?",
"role": "user"
}
],
"index": 146,
"single_turn_completion": "560",
"single_turn_prompt": "Potassium metal can be used as the active surface in a photodiode because electrons are relatively easily removed from a potassium surface. The energy needed is $2.15 \\times 10^{5} J$ per mole of electrons removed ( 1 mole $=6.02 \\times 10^{23}$ electrons). What is the longest wavelength light (in nm) with quanta of sufficient energy to eject electrons from a potassium photodiode surface?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For red light of wavelength $(\\lambda) 6.7102 \\times 10^{-5} cm$, emitted by excited lithium atoms, calculate:\n\nSubproblem 0: the frequency $(v)$ in Hz, to 4 decimal places. \n\n\nSolution: $c=\\lambda v$ and $v=c / \\lambda$ where $v$ is the frequency of radiation (number of waves/s).\nFor: $\\quad \\lambda=6.7102 \\times 10^{-5} cm=6.7102 \\times 10^{-7} m$\n\\[\nv=\\frac{2.9979 \\times 10^{8} {ms}^{-1}}{6.7102 \\times 10^{-7} m}=4.4677 \\times 10^{14} {s}^{-1}= \\boxed{4.4677} Hz\n\\]\n\nFinal answer: The final answer is 4.4677. I hope it is correct.\n\nSubproblem 1: the wave number $(\\bar{v})$ in ${cm}^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 4 decimal places. \n\n\nSolution: $\\bar{v}=\\frac{1}{\\lambda}=\\frac{1}{6.7102 \\times 10^{-7} m}=1.4903 \\times 10^{6} m^{-1}= \\boxed{1.4903e4} {cm}^{-1}$\n\nFinal answer: The final answer is 1.4903e4. I hope it is correct.\n\nSubproblem 2: the wavelength $(\\lambda)$ in nm, to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_147
|
uniq_conv_147
| 1
|
minerva
|
minerva
|
{
"ground_truth": "671.02",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For red light of wavelength $(\\lambda) 6.7102 \\times 10^{-5} cm$, emitted by excited lithium atoms, calculate:\n\nSubproblem 0: the frequency $(v)$ in Hz, to 4 decimal places. \n\n\nSolution: $c=\\lambda v$ and $v=c / \\lambda$ where $v$ is the frequency of radiation (number of waves/s).\nFor: $\\quad \\lambda=6.7102 \\times 10^{-5} cm=6.7102 \\times 10^{-7} m$\n\\[\nv=\\frac{2.9979 \\times 10^{8} {ms}^{-1}}{6.7102 \\times 10^{-7} m}=4.4677 \\times 10^{14} {s}^{-1}= \\boxed{4.4677} Hz\n\\]\n\nFinal answer: The final answer is 4.4677. I hope it is correct.\n\nSubproblem 1: the wave number $(\\bar{v})$ in ${cm}^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 4 decimal places. \n\n\nSolution: $\\bar{v}=\\frac{1}{\\lambda}=\\frac{1}{6.7102 \\times 10^{-7} m}=1.4903 \\times 10^{6} m^{-1}= \\boxed{1.4903e4} {cm}^{-1}$\n\nFinal answer: The final answer is 1.4903e4. I hope it is correct.\n\nSubproblem 2: the wavelength $(\\lambda)$ in nm, to 2 decimal places.",
"role": "user"
}
],
"index": 147,
"single_turn_completion": "671.02",
"single_turn_prompt": "Preamble: For red light of wavelength $(\\lambda) 6.7102 \\times 10^{-5} cm$, emitted by excited lithium atoms, calculate:\n\nSubproblem 0: the frequency $(v)$ in Hz, to 4 decimal places. \n\n\nSolution: $c=\\lambda v$ and $v=c / \\lambda$ where $v$ is the frequency of radiation (number of waves/s).\nFor: $\\quad \\lambda=6.7102 \\times 10^{-5} cm=6.7102 \\times 10^{-7} m$\n\\[\nv=\\frac{2.9979 \\times 10^{8} {ms}^{-1}}{6.7102 \\times 10^{-7} m}=4.4677 \\times 10^{14} {s}^{-1}= \\boxed{4.4677} Hz\n\\]\n\nFinal answer: The final answer is 4.4677. I hope it is correct.\n\nSubproblem 1: the wave number $(\\bar{v})$ in ${cm}^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 4 decimal places. \n\n\nSolution: $\\bar{v}=\\frac{1}{\\lambda}=\\frac{1}{6.7102 \\times 10^{-7} m}=1.4903 \\times 10^{6} m^{-1}= \\boxed{1.4903e4} {cm}^{-1}$\n\nFinal answer: The final answer is 1.4903e4. I hope it is correct.\n\nSubproblem 2: the wavelength $(\\lambda)$ in nm, to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: What is the net charge of arginine in a solution of $\\mathrm{pH} \\mathrm{} 1.0$ ? Please format your answer as +n or -n.",
"role": "user"
}
] | false
|
minerva
|
conv_148
|
uniq_conv_148
| 1
|
minerva
|
minerva
|
{
"ground_truth": "+2",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "What is the net charge of arginine in a solution of $\\mathrm{pH} \\mathrm{} 1.0$ ? Please format your answer as +n or -n.",
"role": "user"
}
],
"index": 148,
"single_turn_completion": "+2",
"single_turn_prompt": "What is the net charge of arginine in a solution of $\\mathrm{pH} \\mathrm{} 1.0$ ? Please format your answer as +n or -n.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For red light of wavelength $(\\lambda) 6.7102 \\times 10^{-5} cm$, emitted by excited lithium atoms, calculate:\n\nSubproblem 0: the frequency $(v)$ in Hz, to 4 decimal places. \n\n\nSolution: $c=\\lambda v$ and $v=c / \\lambda$ where $v$ is the frequency of radiation (number of waves/s).\nFor: $\\quad \\lambda=6.7102 \\times 10^{-5} cm=6.7102 \\times 10^{-7} m$\n\\[\nv=\\frac{2.9979 \\times 10^{8} {ms}^{-1}}{6.7102 \\times 10^{-7} m}=4.4677 \\times 10^{14} {s}^{-1}= \\boxed{4.4677} Hz\n\\]\n\nFinal answer: The final answer is 4.4677. I hope it is correct.\n\nSubproblem 1: the wave number $(\\bar{v})$ in ${cm}^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 4 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_149
|
uniq_conv_149
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.4903e4",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For red light of wavelength $(\\lambda) 6.7102 \\times 10^{-5} cm$, emitted by excited lithium atoms, calculate:\n\nSubproblem 0: the frequency $(v)$ in Hz, to 4 decimal places. \n\n\nSolution: $c=\\lambda v$ and $v=c / \\lambda$ where $v$ is the frequency of radiation (number of waves/s).\nFor: $\\quad \\lambda=6.7102 \\times 10^{-5} cm=6.7102 \\times 10^{-7} m$\n\\[\nv=\\frac{2.9979 \\times 10^{8} {ms}^{-1}}{6.7102 \\times 10^{-7} m}=4.4677 \\times 10^{14} {s}^{-1}= \\boxed{4.4677} Hz\n\\]\n\nFinal answer: The final answer is 4.4677. I hope it is correct.\n\nSubproblem 1: the wave number $(\\bar{v})$ in ${cm}^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 4 decimal places.",
"role": "user"
}
],
"index": 149,
"single_turn_completion": "1.4903e4",
"single_turn_prompt": "Preamble: For red light of wavelength $(\\lambda) 6.7102 \\times 10^{-5} cm$, emitted by excited lithium atoms, calculate:\n\nSubproblem 0: the frequency $(v)$ in Hz, to 4 decimal places. \n\n\nSolution: $c=\\lambda v$ and $v=c / \\lambda$ where $v$ is the frequency of radiation (number of waves/s).\nFor: $\\quad \\lambda=6.7102 \\times 10^{-5} cm=6.7102 \\times 10^{-7} m$\n\\[\nv=\\frac{2.9979 \\times 10^{8} {ms}^{-1}}{6.7102 \\times 10^{-7} m}=4.4677 \\times 10^{14} {s}^{-1}= \\boxed{4.4677} Hz\n\\]\n\nFinal answer: The final answer is 4.4677. I hope it is correct.\n\nSubproblem 1: the wave number $(\\bar{v})$ in ${cm}^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 4 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine the atomic weight of ${He}^{++}$ in amu to 5 decimal places from the values of its constituents.",
"role": "user"
}
] | false
|
minerva
|
conv_150
|
uniq_conv_150
| 1
|
minerva
|
minerva
|
{
"ground_truth": "4.03188",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine the atomic weight of ${He}^{++}$ in amu to 5 decimal places from the values of its constituents.",
"role": "user"
}
],
"index": 150,
"single_turn_completion": "4.03188",
"single_turn_prompt": "Determine the atomic weight of ${He}^{++}$ in amu to 5 decimal places from the values of its constituents.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Determine the following values from a standard radio dial. \n\nSubproblem 0: What is the minimum wavelength in m for broadcasts on the AM band? Format your answer as an integer. \n\n\nSolution: \\[\n\\mathrm{c}=v \\lambda, \\therefore \\lambda_{\\min }=\\frac{\\mathrm{c}}{v_{\\max }} ; \\lambda_{\\max }=\\frac{\\mathrm{c}}{v_{\\min }}\n\\]\n$\\lambda_{\\min }=\\frac{3 \\times 10^{8} m / s}{1600 \\times 10^{3} Hz}=\\boxed{188} m$\n\nFinal answer: The final answer is 188. I hope it is correct.\n\nSubproblem 1: What is the maximum wavelength in m for broadcasts on the AM band? Format your answer as an integer.",
"role": "user"
}
] | false
|
minerva
|
conv_151
|
uniq_conv_151
| 1
|
minerva
|
minerva
|
{
"ground_truth": "566",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Determine the following values from a standard radio dial. \n\nSubproblem 0: What is the minimum wavelength in m for broadcasts on the AM band? Format your answer as an integer. \n\n\nSolution: \\[\n\\mathrm{c}=v \\lambda, \\therefore \\lambda_{\\min }=\\frac{\\mathrm{c}}{v_{\\max }} ; \\lambda_{\\max }=\\frac{\\mathrm{c}}{v_{\\min }}\n\\]\n$\\lambda_{\\min }=\\frac{3 \\times 10^{8} m / s}{1600 \\times 10^{3} Hz}=\\boxed{188} m$\n\nFinal answer: The final answer is 188. I hope it is correct.\n\nSubproblem 1: What is the maximum wavelength in m for broadcasts on the AM band? Format your answer as an integer.",
"role": "user"
}
],
"index": 151,
"single_turn_completion": "566",
"single_turn_prompt": "Preamble: Determine the following values from a standard radio dial. \n\nSubproblem 0: What is the minimum wavelength in m for broadcasts on the AM band? Format your answer as an integer. \n\n\nSolution: \\[\n\\mathrm{c}=v \\lambda, \\therefore \\lambda_{\\min }=\\frac{\\mathrm{c}}{v_{\\max }} ; \\lambda_{\\max }=\\frac{\\mathrm{c}}{v_{\\min }}\n\\]\n$\\lambda_{\\min }=\\frac{3 \\times 10^{8} m / s}{1600 \\times 10^{3} Hz}=\\boxed{188} m$\n\nFinal answer: The final answer is 188. I hope it is correct.\n\nSubproblem 1: What is the maximum wavelength in m for broadcasts on the AM band? Format your answer as an integer.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine the wavelength of radiation emitted by hydrogen atoms in angstroms upon electron transitions from $n=6$ to $n=2$.",
"role": "user"
}
] | false
|
minerva
|
conv_152
|
uniq_conv_152
| 1
|
minerva
|
minerva
|
{
"ground_truth": "4100",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine the wavelength of radiation emitted by hydrogen atoms in angstroms upon electron transitions from $n=6$ to $n=2$.",
"role": "user"
}
],
"index": 152,
"single_turn_completion": "4100",
"single_turn_prompt": "Determine the wavelength of radiation emitted by hydrogen atoms in angstroms upon electron transitions from $n=6$ to $n=2$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Determine the following values from a standard radio dial. \n\nSubproblem 0: What is the minimum wavelength in m for broadcasts on the AM band? Format your answer as an integer. \n\n\nSolution: \\[\n\\mathrm{c}=v \\lambda, \\therefore \\lambda_{\\min }=\\frac{\\mathrm{c}}{v_{\\max }} ; \\lambda_{\\max }=\\frac{\\mathrm{c}}{v_{\\min }}\n\\]\n$\\lambda_{\\min }=\\frac{3 \\times 10^{8} m / s}{1600 \\times 10^{3} Hz}=\\boxed{188} m$\n\nFinal answer: The final answer is 188. I hope it is correct.\n\nSubproblem 1: What is the maximum wavelength in m for broadcasts on the AM band? Format your answer as an integer. \n\n\nSolution: \\[\n\\mathrm{c}=v \\lambda, \\therefore \\lambda_{\\min }=\\frac{\\mathrm{c}}{v_{\\max }} ; \\lambda_{\\max }=\\frac{\\mathrm{c}}{v_{\\min }}\n\\]\n\\[\n\\lambda_{\\max }=\\frac{3 \\times 10^{8}}{530 \\times 10^{3}}=\\boxed{566} m\n\\]\n\nFinal answer: The final answer is 566. I hope it is correct.\n\nSubproblem 2: What is the minimum wavelength in m (to 2 decimal places) for broadcasts on the FM band?",
"role": "user"
}
] | false
|
minerva
|
conv_153
|
uniq_conv_153
| 1
|
minerva
|
minerva
|
{
"ground_truth": "2.78",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Determine the following values from a standard radio dial. \n\nSubproblem 0: What is the minimum wavelength in m for broadcasts on the AM band? Format your answer as an integer. \n\n\nSolution: \\[\n\\mathrm{c}=v \\lambda, \\therefore \\lambda_{\\min }=\\frac{\\mathrm{c}}{v_{\\max }} ; \\lambda_{\\max }=\\frac{\\mathrm{c}}{v_{\\min }}\n\\]\n$\\lambda_{\\min }=\\frac{3 \\times 10^{8} m / s}{1600 \\times 10^{3} Hz}=\\boxed{188} m$\n\nFinal answer: The final answer is 188. I hope it is correct.\n\nSubproblem 1: What is the maximum wavelength in m for broadcasts on the AM band? Format your answer as an integer. \n\n\nSolution: \\[\n\\mathrm{c}=v \\lambda, \\therefore \\lambda_{\\min }=\\frac{\\mathrm{c}}{v_{\\max }} ; \\lambda_{\\max }=\\frac{\\mathrm{c}}{v_{\\min }}\n\\]\n\\[\n\\lambda_{\\max }=\\frac{3 \\times 10^{8}}{530 \\times 10^{3}}=\\boxed{566} m\n\\]\n\nFinal answer: The final answer is 566. I hope it is correct.\n\nSubproblem 2: What is the minimum wavelength in m (to 2 decimal places) for broadcasts on the FM band?",
"role": "user"
}
],
"index": 153,
"single_turn_completion": "2.78",
"single_turn_prompt": "Preamble: Determine the following values from a standard radio dial. \n\nSubproblem 0: What is the minimum wavelength in m for broadcasts on the AM band? Format your answer as an integer. \n\n\nSolution: \\[\n\\mathrm{c}=v \\lambda, \\therefore \\lambda_{\\min }=\\frac{\\mathrm{c}}{v_{\\max }} ; \\lambda_{\\max }=\\frac{\\mathrm{c}}{v_{\\min }}\n\\]\n$\\lambda_{\\min }=\\frac{3 \\times 10^{8} m / s}{1600 \\times 10^{3} Hz}=\\boxed{188} m$\n\nFinal answer: The final answer is 188. I hope it is correct.\n\nSubproblem 1: What is the maximum wavelength in m for broadcasts on the AM band? Format your answer as an integer. \n\n\nSolution: \\[\n\\mathrm{c}=v \\lambda, \\therefore \\lambda_{\\min }=\\frac{\\mathrm{c}}{v_{\\max }} ; \\lambda_{\\max }=\\frac{\\mathrm{c}}{v_{\\min }}\n\\]\n\\[\n\\lambda_{\\max }=\\frac{3 \\times 10^{8}}{530 \\times 10^{3}}=\\boxed{566} m\n\\]\n\nFinal answer: The final answer is 566. I hope it is correct.\n\nSubproblem 2: What is the minimum wavelength in m (to 2 decimal places) for broadcasts on the FM band?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Calculate the \"Bohr radius\" in angstroms to 3 decimal places for ${He}^{+}$.",
"role": "user"
}
] | false
|
minerva
|
conv_154
|
uniq_conv_154
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.264",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Calculate the \"Bohr radius\" in angstroms to 3 decimal places for ${He}^{+}$.",
"role": "user"
}
],
"index": 154,
"single_turn_completion": "0.264",
"single_turn_prompt": "Calculate the \"Bohr radius\" in angstroms to 3 decimal places for ${He}^{+}$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For red light of wavelength $(\\lambda) 6.7102 \\times 10^{-5} cm$, emitted by excited lithium atoms, calculate:\n\nthe frequency $(v)$ in Hz, to 4 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_155
|
uniq_conv_155
| 1
|
minerva
|
minerva
|
{
"ground_truth": "4.4677",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For red light of wavelength $(\\lambda) 6.7102 \\times 10^{-5} cm$, emitted by excited lithium atoms, calculate:\n\nthe frequency $(v)$ in Hz, to 4 decimal places.",
"role": "user"
}
],
"index": 155,
"single_turn_completion": "4.4677",
"single_turn_prompt": "Preamble: For red light of wavelength $(\\lambda) 6.7102 \\times 10^{-5} cm$, emitted by excited lithium atoms, calculate:\n\nthe frequency $(v)$ in Hz, to 4 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Electromagnetic radiation of frequency $3.091 \\times 10^{14} \\mathrm{~Hz}$ illuminates a crystal of germanium (Ge). Calculate the wavelength of photoemission in meters generated by this interaction. Germanium is an elemental semiconductor with a band gap, $E_{g}$, of $0.7 \\mathrm{eV}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_156
|
uniq_conv_156
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.77e-6",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Electromagnetic radiation of frequency $3.091 \\times 10^{14} \\mathrm{~Hz}$ illuminates a crystal of germanium (Ge). Calculate the wavelength of photoemission in meters generated by this interaction. Germanium is an elemental semiconductor with a band gap, $E_{g}$, of $0.7 \\mathrm{eV}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 156,
"single_turn_completion": "1.77e-6",
"single_turn_prompt": "Electromagnetic radiation of frequency $3.091 \\times 10^{14} \\mathrm{~Hz}$ illuminates a crystal of germanium (Ge). Calculate the wavelength of photoemission in meters generated by this interaction. Germanium is an elemental semiconductor with a band gap, $E_{g}$, of $0.7 \\mathrm{eV}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: What is the energy gap (in eV, to 1 decimal place) between the electronic states $n=3$ and $n=8$ in a hydrogen atom?",
"role": "user"
}
] | false
|
minerva
|
conv_157
|
uniq_conv_157
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.3",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "What is the energy gap (in eV, to 1 decimal place) between the electronic states $n=3$ and $n=8$ in a hydrogen atom?",
"role": "user"
}
],
"index": 157,
"single_turn_completion": "1.3",
"single_turn_prompt": "What is the energy gap (in eV, to 1 decimal place) between the electronic states $n=3$ and $n=8$ in a hydrogen atom?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine for hydrogen the velocity in m/s of an electron in an ${n}=4$ state. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_158
|
uniq_conv_158
| 1
|
minerva
|
minerva
|
{
"ground_truth": "5.47e5",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine for hydrogen the velocity in m/s of an electron in an ${n}=4$ state. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 158,
"single_turn_completion": "5.47e5",
"single_turn_prompt": "Determine for hydrogen the velocity in m/s of an electron in an ${n}=4$ state. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: A pure crystalline material (no impurities or dopants are present) appears red in transmitted light.\n\nSubproblem 0: Is this material a conductor, semiconductor or insulator? Give the reasons for your answer.\n\n\nSolution: If the material is pure (no impurity states present), then it must be classified as a \\boxed{semiconductor} since it exhibits a finite \"band gap\" - i.e. to activate charge carriers, photons with energies in excess of \"red\" radiation are required.\n\nFinal answer: The final answer is semiconductor. I hope it is correct.\n\nSubproblem 1: What is the approximate band gap $\\left(\\mathrm{E}_{g}\\right)$ for this material in eV? Please round your answer to 1 decimal place.",
"role": "user"
}
] | false
|
minerva
|
conv_159
|
uniq_conv_159
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.9",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: A pure crystalline material (no impurities or dopants are present) appears red in transmitted light.\n\nSubproblem 0: Is this material a conductor, semiconductor or insulator? Give the reasons for your answer.\n\n\nSolution: If the material is pure (no impurity states present), then it must be classified as a \\boxed{semiconductor} since it exhibits a finite \"band gap\" - i.e. to activate charge carriers, photons with energies in excess of \"red\" radiation are required.\n\nFinal answer: The final answer is semiconductor. I hope it is correct.\n\nSubproblem 1: What is the approximate band gap $\\left(\\mathrm{E}_{g}\\right)$ for this material in eV? Please round your answer to 1 decimal place.",
"role": "user"
}
],
"index": 159,
"single_turn_completion": "1.9",
"single_turn_prompt": "Preamble: A pure crystalline material (no impurities or dopants are present) appears red in transmitted light.\n\nSubproblem 0: Is this material a conductor, semiconductor or insulator? Give the reasons for your answer.\n\n\nSolution: If the material is pure (no impurity states present), then it must be classified as a \\boxed{semiconductor} since it exhibits a finite \"band gap\" - i.e. to activate charge carriers, photons with energies in excess of \"red\" radiation are required.\n\nFinal answer: The final answer is semiconductor. I hope it is correct.\n\nSubproblem 1: What is the approximate band gap $\\left(\\mathrm{E}_{g}\\right)$ for this material in eV? Please round your answer to 1 decimal place.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Calculate the minimum potential $(V)$ in volts (to 1 decimal place) which must be applied to a free electron so that it has enough energy to excite, upon impact, the electron in a hydrogen atom from its ground state to a state of $n=5$.",
"role": "user"
}
] | false
|
minerva
|
conv_160
|
uniq_conv_160
| 1
|
minerva
|
minerva
|
{
"ground_truth": "13.1",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Calculate the minimum potential $(V)$ in volts (to 1 decimal place) which must be applied to a free electron so that it has enough energy to excite, upon impact, the electron in a hydrogen atom from its ground state to a state of $n=5$.",
"role": "user"
}
],
"index": 160,
"single_turn_completion": "13.1",
"single_turn_prompt": "Calculate the minimum potential $(V)$ in volts (to 1 decimal place) which must be applied to a free electron so that it has enough energy to excite, upon impact, the electron in a hydrogen atom from its ground state to a state of $n=5$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For light with a wavelength $(\\lambda)$ of $408 \\mathrm{~nm}$ determine:\n\nSubproblem 0: the frequency in $s^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 3 decimal places. \n\n\nSolution: To solve this problem we must know the following relationships:\n\\[\n\\begin{aligned}\nv \\lambda &=c\n\\end{aligned}\n\\]\n$v$ (frequency) $=\\frac{c}{\\lambda}=\\frac{3 \\times 10^{8} m / s}{408 \\times 10^{-9} m}= \\boxed{7.353e14} s^{-1}$\n\nFinal answer: The final answer is 7.353e14. I hope it is correct.\n\nSubproblem 1: the wave number in $m^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places.\n\n\nSolution: To solve this problem we must know the following relationships:\n\\[\n\\begin{aligned}\n1 / \\lambda=\\bar{v} \n\\end{aligned}\n\\]\n$\\bar{v}$ (wavenumber) $=\\frac{1}{\\lambda}=\\frac{1}{408 \\times 10^{-9} m}=\\boxed{2.45e6} m^{-1}$\n\nFinal answer: The final answer is 2.45e6. I hope it is correct.\n\nSubproblem 2: the wavelength in angstroms.",
"role": "user"
}
] | false
|
minerva
|
conv_161
|
uniq_conv_161
| 1
|
minerva
|
minerva
|
{
"ground_truth": "4080",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For light with a wavelength $(\\lambda)$ of $408 \\mathrm{~nm}$ determine:\n\nSubproblem 0: the frequency in $s^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 3 decimal places. \n\n\nSolution: To solve this problem we must know the following relationships:\n\\[\n\\begin{aligned}\nv \\lambda &=c\n\\end{aligned}\n\\]\n$v$ (frequency) $=\\frac{c}{\\lambda}=\\frac{3 \\times 10^{8} m / s}{408 \\times 10^{-9} m}= \\boxed{7.353e14} s^{-1}$\n\nFinal answer: The final answer is 7.353e14. I hope it is correct.\n\nSubproblem 1: the wave number in $m^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places.\n\n\nSolution: To solve this problem we must know the following relationships:\n\\[\n\\begin{aligned}\n1 / \\lambda=\\bar{v} \n\\end{aligned}\n\\]\n$\\bar{v}$ (wavenumber) $=\\frac{1}{\\lambda}=\\frac{1}{408 \\times 10^{-9} m}=\\boxed{2.45e6} m^{-1}$\n\nFinal answer: The final answer is 2.45e6. I hope it is correct.\n\nSubproblem 2: the wavelength in angstroms.",
"role": "user"
}
],
"index": 161,
"single_turn_completion": "4080",
"single_turn_prompt": "Preamble: For light with a wavelength $(\\lambda)$ of $408 \\mathrm{~nm}$ determine:\n\nSubproblem 0: the frequency in $s^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 3 decimal places. \n\n\nSolution: To solve this problem we must know the following relationships:\n\\[\n\\begin{aligned}\nv \\lambda &=c\n\\end{aligned}\n\\]\n$v$ (frequency) $=\\frac{c}{\\lambda}=\\frac{3 \\times 10^{8} m / s}{408 \\times 10^{-9} m}= \\boxed{7.353e14} s^{-1}$\n\nFinal answer: The final answer is 7.353e14. I hope it is correct.\n\nSubproblem 1: the wave number in $m^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places.\n\n\nSolution: To solve this problem we must know the following relationships:\n\\[\n\\begin{aligned}\n1 / \\lambda=\\bar{v} \n\\end{aligned}\n\\]\n$\\bar{v}$ (wavenumber) $=\\frac{1}{\\lambda}=\\frac{1}{408 \\times 10^{-9} m}=\\boxed{2.45e6} m^{-1}$\n\nFinal answer: The final answer is 2.45e6. I hope it is correct.\n\nSubproblem 2: the wavelength in angstroms.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Reference the information below to solve the following problems. \n$\\begin{array}{llll}\\text { Element } & \\text { Ionization Potential } & \\text { Element } & \\text { Ionization Potential } \\\\ {Na} & 5.14 & {Ca} & 6.11 \\\\ {Mg} & 7.64 & {Sc} & 6.54 \\\\ {Al} & 5.98 & {Ti} & 6.82 \\\\ {Si} & 8.15 & {~V} & 6.74 \\\\ {P} & 10.48 & {Cr} & 6.76 \\\\ {~S} & 10.36 & {Mn} & 7.43 \\\\ {Cl} & 13.01 & {Fe} & 7.9 \\\\ {Ar} & 15.75 & {Co} & 7.86 \\\\ & & {Ni} & 7.63 \\\\ & & {Cu} & 7.72\\end{array}$\n\nSubproblem 0: What is the first ionization energy (in J, to 3 decimal places) for Na?\n\n\nSolution: The required data can be obtained by multiplying the ionization potentials (listed in the Periodic Table) with the electronic charge ( ${e}^{-}=1.6 \\times 10^{-19}$ C).\n\\boxed{0.822} J.\n\nFinal answer: The final answer is 0.822. I hope it is correct.\n\nSubproblem 1: What is the first ionization energy (in J, to 2 decimal places) for Mg?",
"role": "user"
}
] | false
|
minerva
|
conv_162
|
uniq_conv_162
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.22",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Reference the information below to solve the following problems. \n$\\begin{array}{llll}\\text { Element } & \\text { Ionization Potential } & \\text { Element } & \\text { Ionization Potential } \\\\ {Na} & 5.14 & {Ca} & 6.11 \\\\ {Mg} & 7.64 & {Sc} & 6.54 \\\\ {Al} & 5.98 & {Ti} & 6.82 \\\\ {Si} & 8.15 & {~V} & 6.74 \\\\ {P} & 10.48 & {Cr} & 6.76 \\\\ {~S} & 10.36 & {Mn} & 7.43 \\\\ {Cl} & 13.01 & {Fe} & 7.9 \\\\ {Ar} & 15.75 & {Co} & 7.86 \\\\ & & {Ni} & 7.63 \\\\ & & {Cu} & 7.72\\end{array}$\n\nSubproblem 0: What is the first ionization energy (in J, to 3 decimal places) for Na?\n\n\nSolution: The required data can be obtained by multiplying the ionization potentials (listed in the Periodic Table) with the electronic charge ( ${e}^{-}=1.6 \\times 10^{-19}$ C).\n\\boxed{0.822} J.\n\nFinal answer: The final answer is 0.822. I hope it is correct.\n\nSubproblem 1: What is the first ionization energy (in J, to 2 decimal places) for Mg?",
"role": "user"
}
],
"index": 162,
"single_turn_completion": "1.22",
"single_turn_prompt": "Preamble: Reference the information below to solve the following problems. \n$\\begin{array}{llll}\\text { Element } & \\text { Ionization Potential } & \\text { Element } & \\text { Ionization Potential } \\\\ {Na} & 5.14 & {Ca} & 6.11 \\\\ {Mg} & 7.64 & {Sc} & 6.54 \\\\ {Al} & 5.98 & {Ti} & 6.82 \\\\ {Si} & 8.15 & {~V} & 6.74 \\\\ {P} & 10.48 & {Cr} & 6.76 \\\\ {~S} & 10.36 & {Mn} & 7.43 \\\\ {Cl} & 13.01 & {Fe} & 7.9 \\\\ {Ar} & 15.75 & {Co} & 7.86 \\\\ & & {Ni} & 7.63 \\\\ & & {Cu} & 7.72\\end{array}$\n\nSubproblem 0: What is the first ionization energy (in J, to 3 decimal places) for Na?\n\n\nSolution: The required data can be obtained by multiplying the ionization potentials (listed in the Periodic Table) with the electronic charge ( ${e}^{-}=1.6 \\times 10^{-19}$ C).\n\\boxed{0.822} J.\n\nFinal answer: The final answer is 0.822. I hope it is correct.\n\nSubproblem 1: What is the first ionization energy (in J, to 2 decimal places) for Mg?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Light of wavelength $\\lambda=4.28 \\times 10^{-7} {~m}$ interacts with a \"motionless\" hydrogen atom. During this interaction it transfers all its energy to the orbiting electron of the hydrogen. What is the velocity in m/s of this electron after interaction? Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_163
|
uniq_conv_163
| 1
|
minerva
|
minerva
|
{
"ground_truth": "2.19e6",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Light of wavelength $\\lambda=4.28 \\times 10^{-7} {~m}$ interacts with a \"motionless\" hydrogen atom. During this interaction it transfers all its energy to the orbiting electron of the hydrogen. What is the velocity in m/s of this electron after interaction? Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 163,
"single_turn_completion": "2.19e6",
"single_turn_prompt": "Light of wavelength $\\lambda=4.28 \\times 10^{-7} {~m}$ interacts with a \"motionless\" hydrogen atom. During this interaction it transfers all its energy to the orbiting electron of the hydrogen. What is the velocity in m/s of this electron after interaction? Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine the minimum potential in V (to 2 decimal places) that must be applied to an $\\alpha$-particle so that on interaction with a hydrogen atom, a ground state electron will be excited to $n$ $=6$.",
"role": "user"
}
] | false
|
minerva
|
conv_164
|
uniq_conv_164
| 1
|
minerva
|
minerva
|
{
"ground_truth": "6.62",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine the minimum potential in V (to 2 decimal places) that must be applied to an $\\alpha$-particle so that on interaction with a hydrogen atom, a ground state electron will be excited to $n$ $=6$.",
"role": "user"
}
],
"index": 164,
"single_turn_completion": "6.62",
"single_turn_prompt": "Determine the minimum potential in V (to 2 decimal places) that must be applied to an $\\alpha$-particle so that on interaction with a hydrogen atom, a ground state electron will be excited to $n$ $=6$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Reference the information below to solve the following problems. \n$\\begin{array}{llll}\\text { Element } & \\text { Ionization Potential } & \\text { Element } & \\text { Ionization Potential } \\\\ {Na} & 5.14 & {Ca} & 6.11 \\\\ {Mg} & 7.64 & {Sc} & 6.54 \\\\ {Al} & 5.98 & {Ti} & 6.82 \\\\ {Si} & 8.15 & {~V} & 6.74 \\\\ {P} & 10.48 & {Cr} & 6.76 \\\\ {~S} & 10.36 & {Mn} & 7.43 \\\\ {Cl} & 13.01 & {Fe} & 7.9 \\\\ {Ar} & 15.75 & {Co} & 7.86 \\\\ & & {Ni} & 7.63 \\\\ & & {Cu} & 7.72\\end{array}$\n\nWhat is the first ionization energy (in J, to 3 decimal places) for Na?",
"role": "user"
}
] | false
|
minerva
|
conv_165
|
uniq_conv_165
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.822",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Reference the information below to solve the following problems. \n$\\begin{array}{llll}\\text { Element } & \\text { Ionization Potential } & \\text { Element } & \\text { Ionization Potential } \\\\ {Na} & 5.14 & {Ca} & 6.11 \\\\ {Mg} & 7.64 & {Sc} & 6.54 \\\\ {Al} & 5.98 & {Ti} & 6.82 \\\\ {Si} & 8.15 & {~V} & 6.74 \\\\ {P} & 10.48 & {Cr} & 6.76 \\\\ {~S} & 10.36 & {Mn} & 7.43 \\\\ {Cl} & 13.01 & {Fe} & 7.9 \\\\ {Ar} & 15.75 & {Co} & 7.86 \\\\ & & {Ni} & 7.63 \\\\ & & {Cu} & 7.72\\end{array}$\n\nWhat is the first ionization energy (in J, to 3 decimal places) for Na?",
"role": "user"
}
],
"index": 165,
"single_turn_completion": "0.822",
"single_turn_prompt": "Preamble: Reference the information below to solve the following problems. \n$\\begin{array}{llll}\\text { Element } & \\text { Ionization Potential } & \\text { Element } & \\text { Ionization Potential } \\\\ {Na} & 5.14 & {Ca} & 6.11 \\\\ {Mg} & 7.64 & {Sc} & 6.54 \\\\ {Al} & 5.98 & {Ti} & 6.82 \\\\ {Si} & 8.15 & {~V} & 6.74 \\\\ {P} & 10.48 & {Cr} & 6.76 \\\\ {~S} & 10.36 & {Mn} & 7.43 \\\\ {Cl} & 13.01 & {Fe} & 7.9 \\\\ {Ar} & 15.75 & {Co} & 7.86 \\\\ & & {Ni} & 7.63 \\\\ & & {Cu} & 7.72\\end{array}$\n\nWhat is the first ionization energy (in J, to 3 decimal places) for Na?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For \"yellow radiation\" (frequency, $v,=5.09 \\times 10^{14} s^{-1}$ ) emitted by activated sodium, determine:\n\nSubproblem 0: the wavelength $(\\lambda)$ in m. Please format your answer as $n \\times 10^x$, where n is to 2 decimal places.\n\n\nSolution: The equation relating $v$ and $\\lambda$ is $c=v \\lambda$ where $c$ is the speed of light $=3.00 \\times 10^{8} \\mathrm{~m}$.\n\\[\n\\lambda=\\frac{c}{v}=\\frac{3.00 \\times 10^{8} m / s}{5.09 \\times 10^{14} s^{-1}}=\\boxed{5.89e-7} m\n\\]\n\nFinal answer: The final answer is 5.89e-7. I hope it is correct.\n\nSubproblem 1: the wave number $(\\bar{v})$ in ${cm}^{-1}$. Please format your answer as $n \\times 10^x$, where n is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_166
|
uniq_conv_166
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.70e4",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For \"yellow radiation\" (frequency, $v,=5.09 \\times 10^{14} s^{-1}$ ) emitted by activated sodium, determine:\n\nSubproblem 0: the wavelength $(\\lambda)$ in m. Please format your answer as $n \\times 10^x$, where n is to 2 decimal places.\n\n\nSolution: The equation relating $v$ and $\\lambda$ is $c=v \\lambda$ where $c$ is the speed of light $=3.00 \\times 10^{8} \\mathrm{~m}$.\n\\[\n\\lambda=\\frac{c}{v}=\\frac{3.00 \\times 10^{8} m / s}{5.09 \\times 10^{14} s^{-1}}=\\boxed{5.89e-7} m\n\\]\n\nFinal answer: The final answer is 5.89e-7. I hope it is correct.\n\nSubproblem 1: the wave number $(\\bar{v})$ in ${cm}^{-1}$. Please format your answer as $n \\times 10^x$, where n is to 2 decimal places.",
"role": "user"
}
],
"index": 166,
"single_turn_completion": "1.70e4",
"single_turn_prompt": "Preamble: For \"yellow radiation\" (frequency, $v,=5.09 \\times 10^{14} s^{-1}$ ) emitted by activated sodium, determine:\n\nSubproblem 0: the wavelength $(\\lambda)$ in m. Please format your answer as $n \\times 10^x$, where n is to 2 decimal places.\n\n\nSolution: The equation relating $v$ and $\\lambda$ is $c=v \\lambda$ where $c$ is the speed of light $=3.00 \\times 10^{8} \\mathrm{~m}$.\n\\[\n\\lambda=\\frac{c}{v}=\\frac{3.00 \\times 10^{8} m / s}{5.09 \\times 10^{14} s^{-1}}=\\boxed{5.89e-7} m\n\\]\n\nFinal answer: The final answer is 5.89e-7. I hope it is correct.\n\nSubproblem 1: the wave number $(\\bar{v})$ in ${cm}^{-1}$. Please format your answer as $n \\times 10^x$, where n is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Subproblem 0: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{O}_{2}$ (in decimal form)?",
"role": "user"
}
] | false
|
minerva
|
conv_167
|
uniq_conv_167
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.5",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Subproblem 0: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{O}_{2}$ (in decimal form)?",
"role": "user"
}
],
"index": 167,
"single_turn_completion": "0.5",
"single_turn_prompt": "Subproblem 0: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{O}_{2}$ (in decimal form)?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Calculate the molecular weight in g/mole (to 2 decimal places) of each of the substances listed below.\n\n$\\mathrm{NH}_{4} \\mathrm{OH}$",
"role": "user"
}
] | false
|
minerva
|
conv_168
|
uniq_conv_168
| 1
|
minerva
|
minerva
|
{
"ground_truth": "35.06",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Calculate the molecular weight in g/mole (to 2 decimal places) of each of the substances listed below.\n\n$\\mathrm{NH}_{4} \\mathrm{OH}$",
"role": "user"
}
],
"index": 168,
"single_turn_completion": "35.06",
"single_turn_prompt": "Preamble: Calculate the molecular weight in g/mole (to 2 decimal places) of each of the substances listed below.\n\n$\\mathrm{NH}_{4} \\mathrm{OH}$",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Subproblem 0: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{O}_{2}$ (in decimal form)?\n\n\nSolution: \\boxed{0.5}. \n\nFinal answer: The final answer is 0.5. I hope it is correct.\n\nSubproblem 2: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}_{2}$ (in decimal form)?",
"role": "user"
}
] | false
|
minerva
|
conv_169
|
uniq_conv_169
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Subproblem 0: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{O}_{2}$ (in decimal form)?\n\n\nSolution: \\boxed{0.5}. \n\nFinal answer: The final answer is 0.5. I hope it is correct.\n\nSubproblem 2: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}_{2}$ (in decimal form)?",
"role": "user"
}
],
"index": 169,
"single_turn_completion": "1",
"single_turn_prompt": "Subproblem 0: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{O}_{2}$ (in decimal form)?\n\n\nSolution: \\boxed{0.5}. \n\nFinal answer: The final answer is 0.5. I hope it is correct.\n\nSubproblem 2: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}_{2}$ (in decimal form)?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Magnesium (Mg) has the following isotopic distribution:\n\\[\n\\begin{array}{ll}\n24_{\\mathrm{Mg}} & 23.985 \\mathrm{amu} \\text { at } 0.7870 \\text { fractional abundance } \\\\\n25_{\\mathrm{Mg}} & 24.986 \\mathrm{amu} \\text { at } 0.1013 \\text { fractional abundance } \\\\\n26_{\\mathrm{Mg}} & 25.983 \\mathrm{amu} \\text { at } 0.1117 \\text { fractional abundance }\n\\end{array}\n\\]\nWhat is the atomic weight of magnesium (Mg) (to 3 decimal places) according to these data?",
"role": "user"
}
] | false
|
minerva
|
conv_170
|
uniq_conv_170
| 1
|
minerva
|
minerva
|
{
"ground_truth": "24.310",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Magnesium (Mg) has the following isotopic distribution:\n\\[\n\\begin{array}{ll}\n24_{\\mathrm{Mg}} & 23.985 \\mathrm{amu} \\text { at } 0.7870 \\text { fractional abundance } \\\\\n25_{\\mathrm{Mg}} & 24.986 \\mathrm{amu} \\text { at } 0.1013 \\text { fractional abundance } \\\\\n26_{\\mathrm{Mg}} & 25.983 \\mathrm{amu} \\text { at } 0.1117 \\text { fractional abundance }\n\\end{array}\n\\]\nWhat is the atomic weight of magnesium (Mg) (to 3 decimal places) according to these data?",
"role": "user"
}
],
"index": 170,
"single_turn_completion": "24.310",
"single_turn_prompt": "Magnesium (Mg) has the following isotopic distribution:\n\\[\n\\begin{array}{ll}\n24_{\\mathrm{Mg}} & 23.985 \\mathrm{amu} \\text { at } 0.7870 \\text { fractional abundance } \\\\\n25_{\\mathrm{Mg}} & 24.986 \\mathrm{amu} \\text { at } 0.1013 \\text { fractional abundance } \\\\\n26_{\\mathrm{Mg}} & 25.983 \\mathrm{amu} \\text { at } 0.1117 \\text { fractional abundance }\n\\end{array}\n\\]\nWhat is the atomic weight of magnesium (Mg) (to 3 decimal places) according to these data?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Electrons are accelerated by a potential of 10 Volts.\n\nDetermine their velocity in m/s. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_171
|
uniq_conv_171
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.87e6",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Electrons are accelerated by a potential of 10 Volts.\n\nDetermine their velocity in m/s. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 171,
"single_turn_completion": "1.87e6",
"single_turn_prompt": "Preamble: Electrons are accelerated by a potential of 10 Volts.\n\nDetermine their velocity in m/s. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine the frequency (in $s^{-1}$ of radiation capable of generating, in atomic hydrogen, free electrons which have a velocity of $1.3 \\times 10^{6} {~ms}^{-1}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_172
|
uniq_conv_172
| 1
|
minerva
|
minerva
|
{
"ground_truth": "4.45e15",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine the frequency (in $s^{-1}$ of radiation capable of generating, in atomic hydrogen, free electrons which have a velocity of $1.3 \\times 10^{6} {~ms}^{-1}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 172,
"single_turn_completion": "4.45e15",
"single_turn_prompt": "Determine the frequency (in $s^{-1}$ of radiation capable of generating, in atomic hydrogen, free electrons which have a velocity of $1.3 \\times 10^{6} {~ms}^{-1}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?",
"role": "user"
}
] | false
|
minerva
|
conv_173
|
uniq_conv_173
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?",
"role": "user"
}
],
"index": 173,
"single_turn_completion": "1",
"single_turn_prompt": "In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Electrons are accelerated by a potential of 10 Volts.\n\nSubproblem 0: Determine their velocity in m/s. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places. \n\n\nSolution: The definition of an ${eV}$ is the energy gained by an electron when it is accelerated through a potential of $1 {~V}$, so an electron accelerated by a potential of $10 {~V}$ would have an energy of $10 {eV}$.\\\\\n${E}=\\frac{1}{2} m {v}^{2} \\rightarrow {v}=\\sqrt{2 {E} / {m}}$\n\\[\nE=10 {eV}=1.60 \\times 10^{-18} {~J}\n\\]\n\\[\n\\begin{aligned}\n& {m}=\\text { mass of electron }=9.11 \\times 10^{-31} {~kg} \\\\\n& v=\\sqrt{\\frac{2 \\times 1.6 \\times 10^{-18} {~J}}{9.11 \\times 10^{-31} {~kg}}}= \\boxed{1.87e6} {~m} / {s} \n\\end{aligned}\n\\]\n\nFinal answer: The final answer is 1.87e6. I hope it is correct.\n\nSubproblem 1: Determine their deBroglie wavelength $\\left(\\lambda_{p}\\right)$ in m. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_174
|
uniq_conv_174
| 1
|
minerva
|
minerva
|
{
"ground_truth": "3.89e-10",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Electrons are accelerated by a potential of 10 Volts.\n\nSubproblem 0: Determine their velocity in m/s. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places. \n\n\nSolution: The definition of an ${eV}$ is the energy gained by an electron when it is accelerated through a potential of $1 {~V}$, so an electron accelerated by a potential of $10 {~V}$ would have an energy of $10 {eV}$.\\\\\n${E}=\\frac{1}{2} m {v}^{2} \\rightarrow {v}=\\sqrt{2 {E} / {m}}$\n\\[\nE=10 {eV}=1.60 \\times 10^{-18} {~J}\n\\]\n\\[\n\\begin{aligned}\n& {m}=\\text { mass of electron }=9.11 \\times 10^{-31} {~kg} \\\\\n& v=\\sqrt{\\frac{2 \\times 1.6 \\times 10^{-18} {~J}}{9.11 \\times 10^{-31} {~kg}}}= \\boxed{1.87e6} {~m} / {s} \n\\end{aligned}\n\\]\n\nFinal answer: The final answer is 1.87e6. I hope it is correct.\n\nSubproblem 1: Determine their deBroglie wavelength $\\left(\\lambda_{p}\\right)$ in m. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 174,
"single_turn_completion": "3.89e-10",
"single_turn_prompt": "Preamble: Electrons are accelerated by a potential of 10 Volts.\n\nSubproblem 0: Determine their velocity in m/s. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places. \n\n\nSolution: The definition of an ${eV}$ is the energy gained by an electron when it is accelerated through a potential of $1 {~V}$, so an electron accelerated by a potential of $10 {~V}$ would have an energy of $10 {eV}$.\\\\\n${E}=\\frac{1}{2} m {v}^{2} \\rightarrow {v}=\\sqrt{2 {E} / {m}}$\n\\[\nE=10 {eV}=1.60 \\times 10^{-18} {~J}\n\\]\n\\[\n\\begin{aligned}\n& {m}=\\text { mass of electron }=9.11 \\times 10^{-31} {~kg} \\\\\n& v=\\sqrt{\\frac{2 \\times 1.6 \\times 10^{-18} {~J}}{9.11 \\times 10^{-31} {~kg}}}= \\boxed{1.87e6} {~m} / {s} \n\\end{aligned}\n\\]\n\nFinal answer: The final answer is 1.87e6. I hope it is correct.\n\nSubproblem 1: Determine their deBroglie wavelength $\\left(\\lambda_{p}\\right)$ in m. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: In all likelihood, the Soviet Union and the United States together in the past exploded about ten hydrogen devices underground per year.\n\nIf each explosion converted about $10 \\mathrm{~g}$ of matter into an equivalent amount of energy (a conservative estimate), how many $k J$ of energy were released per device? Please format your answer as $n \\times 10^{x}$.",
"role": "user"
}
] | false
|
minerva
|
conv_175
|
uniq_conv_175
| 1
|
minerva
|
minerva
|
{
"ground_truth": "9e11",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: In all likelihood, the Soviet Union and the United States together in the past exploded about ten hydrogen devices underground per year.\n\nIf each explosion converted about $10 \\mathrm{~g}$ of matter into an equivalent amount of energy (a conservative estimate), how many $k J$ of energy were released per device? Please format your answer as $n \\times 10^{x}$.",
"role": "user"
}
],
"index": 175,
"single_turn_completion": "9e11",
"single_turn_prompt": "Preamble: In all likelihood, the Soviet Union and the United States together in the past exploded about ten hydrogen devices underground per year.\n\nIf each explosion converted about $10 \\mathrm{~g}$ of matter into an equivalent amount of energy (a conservative estimate), how many $k J$ of energy were released per device? Please format your answer as $n \\times 10^{x}$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Calculate the molecular weight in g/mole (to 2 decimal places) of each of the substances listed below.\n\nSubproblem 0: $\\mathrm{NH}_{4} \\mathrm{OH}$\n\n\nSolution: $\\mathrm{NH}_{4} \\mathrm{OH}$ :\n$5 \\times 1.01=5.05(\\mathrm{H})$\n$1 \\times 14.01=14.01(\\mathrm{~N})$\n$1 \\times 16.00=16.00(\\mathrm{O})$\n$\\mathrm{NH}_{4} \\mathrm{OH}= \\boxed{35.06}$ g/mole\n\nFinal answer: The final answer is 35.06. I hope it is correct.\n\nSubproblem 1: $\\mathrm{NaHCO}_{3}$\n\n\nSolution: $\\mathrm{NaHCO}_{3}: 3 \\times 16.00=48.00(\\mathrm{O})$\n$1 \\times 22.99=22.99(\\mathrm{Na})$\n$1 \\times 1.01=1.01$ (H)\n$1 \\times 12.01=12.01$ (C)\n$\\mathrm{NaHCO}_{3}= \\boxed{84.01}$ g/mole\n\nFinal answer: The final answer is 84.01. I hope it is correct.\n\nSubproblem 2: $\\mathrm{CH}_{3} \\mathrm{CH}_{2} \\mathrm{OH}$",
"role": "user"
}
] | false
|
minerva
|
conv_176
|
uniq_conv_176
| 1
|
minerva
|
minerva
|
{
"ground_truth": "46.08",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Calculate the molecular weight in g/mole (to 2 decimal places) of each of the substances listed below.\n\nSubproblem 0: $\\mathrm{NH}_{4} \\mathrm{OH}$\n\n\nSolution: $\\mathrm{NH}_{4} \\mathrm{OH}$ :\n$5 \\times 1.01=5.05(\\mathrm{H})$\n$1 \\times 14.01=14.01(\\mathrm{~N})$\n$1 \\times 16.00=16.00(\\mathrm{O})$\n$\\mathrm{NH}_{4} \\mathrm{OH}= \\boxed{35.06}$ g/mole\n\nFinal answer: The final answer is 35.06. I hope it is correct.\n\nSubproblem 1: $\\mathrm{NaHCO}_{3}$\n\n\nSolution: $\\mathrm{NaHCO}_{3}: 3 \\times 16.00=48.00(\\mathrm{O})$\n$1 \\times 22.99=22.99(\\mathrm{Na})$\n$1 \\times 1.01=1.01$ (H)\n$1 \\times 12.01=12.01$ (C)\n$\\mathrm{NaHCO}_{3}= \\boxed{84.01}$ g/mole\n\nFinal answer: The final answer is 84.01. I hope it is correct.\n\nSubproblem 2: $\\mathrm{CH}_{3} \\mathrm{CH}_{2} \\mathrm{OH}$",
"role": "user"
}
],
"index": 176,
"single_turn_completion": "46.08",
"single_turn_prompt": "Preamble: Calculate the molecular weight in g/mole (to 2 decimal places) of each of the substances listed below.\n\nSubproblem 0: $\\mathrm{NH}_{4} \\mathrm{OH}$\n\n\nSolution: $\\mathrm{NH}_{4} \\mathrm{OH}$ :\n$5 \\times 1.01=5.05(\\mathrm{H})$\n$1 \\times 14.01=14.01(\\mathrm{~N})$\n$1 \\times 16.00=16.00(\\mathrm{O})$\n$\\mathrm{NH}_{4} \\mathrm{OH}= \\boxed{35.06}$ g/mole\n\nFinal answer: The final answer is 35.06. I hope it is correct.\n\nSubproblem 1: $\\mathrm{NaHCO}_{3}$\n\n\nSolution: $\\mathrm{NaHCO}_{3}: 3 \\times 16.00=48.00(\\mathrm{O})$\n$1 \\times 22.99=22.99(\\mathrm{Na})$\n$1 \\times 1.01=1.01$ (H)\n$1 \\times 12.01=12.01$ (C)\n$\\mathrm{NaHCO}_{3}= \\boxed{84.01}$ g/mole\n\nFinal answer: The final answer is 84.01. I hope it is correct.\n\nSubproblem 2: $\\mathrm{CH}_{3} \\mathrm{CH}_{2} \\mathrm{OH}$",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Subproblem 0: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{O}_{2}$ (in decimal form)?\n\n\nSolution: \\boxed{0.5}. \n\nFinal answer: The final answer is 0.5. I hope it is correct.\n\nSubproblem 2: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}_{2}$ (in decimal form)?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 3: If $32.0 \\mathrm{~g}$ of oxygen react with $\\mathrm{CO}$ to form carbon dioxide $\\left(\\mathrm{CO}_{2}\\right)$, how much CO was consumed in this reaction (to 1 decimal place)?",
"role": "user"
}
] | false
|
minerva
|
conv_177
|
uniq_conv_177
| 1
|
minerva
|
minerva
|
{
"ground_truth": "56.0",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Subproblem 0: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{O}_{2}$ (in decimal form)?\n\n\nSolution: \\boxed{0.5}. \n\nFinal answer: The final answer is 0.5. I hope it is correct.\n\nSubproblem 2: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}_{2}$ (in decimal form)?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 3: If $32.0 \\mathrm{~g}$ of oxygen react with $\\mathrm{CO}$ to form carbon dioxide $\\left(\\mathrm{CO}_{2}\\right)$, how much CO was consumed in this reaction (to 1 decimal place)?",
"role": "user"
}
],
"index": 177,
"single_turn_completion": "56.0",
"single_turn_prompt": "Subproblem 0: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}$?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 1: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{O}_{2}$ (in decimal form)?\n\n\nSolution: \\boxed{0.5}. \n\nFinal answer: The final answer is 0.5. I hope it is correct.\n\nSubproblem 2: In the balanced equation for the reaction between $\\mathrm{CO}$ and $\\mathrm{O}_{2}$ to form $\\mathrm{CO}_{2}$, what is the coefficient of $\\mathrm{CO}_{2}$ (in decimal form)?\n\n\nSolution: \\boxed{1}.\n\nFinal answer: The final answer is 1. I hope it is correct.\n\nSubproblem 3: If $32.0 \\mathrm{~g}$ of oxygen react with $\\mathrm{CO}$ to form carbon dioxide $\\left(\\mathrm{CO}_{2}\\right)$, how much CO was consumed in this reaction (to 1 decimal place)?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For \"yellow radiation\" (frequency, $v,=5.09 \\times 10^{14} s^{-1}$ ) emitted by activated sodium, determine:\n\nthe wavelength $(\\lambda)$ in m. Please format your answer as $n \\times 10^x$, where n is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_178
|
uniq_conv_178
| 1
|
minerva
|
minerva
|
{
"ground_truth": "5.89e-7",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For \"yellow radiation\" (frequency, $v,=5.09 \\times 10^{14} s^{-1}$ ) emitted by activated sodium, determine:\n\nthe wavelength $(\\lambda)$ in m. Please format your answer as $n \\times 10^x$, where n is to 2 decimal places.",
"role": "user"
}
],
"index": 178,
"single_turn_completion": "5.89e-7",
"single_turn_prompt": "Preamble: For \"yellow radiation\" (frequency, $v,=5.09 \\times 10^{14} s^{-1}$ ) emitted by activated sodium, determine:\n\nthe wavelength $(\\lambda)$ in m. Please format your answer as $n \\times 10^x$, where n is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: For a proton which has been subjected to an accelerating potential (V) of 15 Volts, determine its deBroglie wavelength in m. Please format your answer as $n \\times 10^x$, where $n$ is to 1 decimal place.",
"role": "user"
}
] | false
|
minerva
|
conv_179
|
uniq_conv_179
| 1
|
minerva
|
minerva
|
{
"ground_truth": "7.4e-12",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "For a proton which has been subjected to an accelerating potential (V) of 15 Volts, determine its deBroglie wavelength in m. Please format your answer as $n \\times 10^x$, where $n$ is to 1 decimal place.",
"role": "user"
}
],
"index": 179,
"single_turn_completion": "7.4e-12",
"single_turn_prompt": "For a proton which has been subjected to an accelerating potential (V) of 15 Volts, determine its deBroglie wavelength in m. Please format your answer as $n \\times 10^x$, where $n$ is to 1 decimal place.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For light with a wavelength $(\\lambda)$ of $408 \\mathrm{~nm}$ determine:\n\nthe frequency in $s^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 3 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_180
|
uniq_conv_180
| 1
|
minerva
|
minerva
|
{
"ground_truth": "7.353e14",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For light with a wavelength $(\\lambda)$ of $408 \\mathrm{~nm}$ determine:\n\nthe frequency in $s^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 3 decimal places.",
"role": "user"
}
],
"index": 180,
"single_turn_completion": "7.353e14",
"single_turn_prompt": "Preamble: For light with a wavelength $(\\lambda)$ of $408 \\mathrm{~nm}$ determine:\n\nthe frequency in $s^{-1}$. Please format your answer as $n \\times 10^x$, where $n$ is to 3 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine in units of eV (to 2 decimal places) the energy of a photon ( $h v)$ with the wavelength of $800$ nm.",
"role": "user"
}
] | false
|
minerva
|
conv_181
|
uniq_conv_181
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.55",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine in units of eV (to 2 decimal places) the energy of a photon ( $h v)$ with the wavelength of $800$ nm.",
"role": "user"
}
],
"index": 181,
"single_turn_completion": "1.55",
"single_turn_prompt": "Determine in units of eV (to 2 decimal places) the energy of a photon ( $h v)$ with the wavelength of $800$ nm.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine for barium (Ba) the linear density of atoms along the $<110>$ directions, in atoms/m.",
"role": "user"
}
] | false
|
minerva
|
conv_182
|
uniq_conv_182
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.39e9",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine for barium (Ba) the linear density of atoms along the $<110>$ directions, in atoms/m.",
"role": "user"
}
],
"index": 182,
"single_turn_completion": "1.39e9",
"single_turn_prompt": "Determine for barium (Ba) the linear density of atoms along the $<110>$ directions, in atoms/m.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: A photon with a wavelength $(\\lambda)$ of $3.091 \\times 10^{-7} {~m}$ strikes an atom of hydrogen. Determine the velocity in m/s of an electron ejected from the excited state, $n=3$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_183
|
uniq_conv_183
| 1
|
minerva
|
minerva
|
{
"ground_truth": "9.35e5",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A photon with a wavelength $(\\lambda)$ of $3.091 \\times 10^{-7} {~m}$ strikes an atom of hydrogen. Determine the velocity in m/s of an electron ejected from the excited state, $n=3$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 183,
"single_turn_completion": "9.35e5",
"single_turn_prompt": "A photon with a wavelength $(\\lambda)$ of $3.091 \\times 10^{-7} {~m}$ strikes an atom of hydrogen. Determine the velocity in m/s of an electron ejected from the excited state, $n=3$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For the element copper (Cu) determine:\n\nthe distance of second nearest neighbors (in meters). Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_184
|
uniq_conv_184
| 1
|
minerva
|
minerva
|
{
"ground_truth": "3.61e-10",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For the element copper (Cu) determine:\n\nthe distance of second nearest neighbors (in meters). Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 184,
"single_turn_completion": "3.61e-10",
"single_turn_prompt": "Preamble: For the element copper (Cu) determine:\n\nthe distance of second nearest neighbors (in meters). Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: A line of the Lyman series of the spectrum of hydrogen has a wavelength of $9.50 \\times 10^{-8} {~m}$. What was the \"upper\" quantum state $\\left({n}_{{i}}\\right)$ involved in the associated electron transition?",
"role": "user"
}
] | false
|
minerva
|
conv_185
|
uniq_conv_185
| 1
|
minerva
|
minerva
|
{
"ground_truth": "5",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A line of the Lyman series of the spectrum of hydrogen has a wavelength of $9.50 \\times 10^{-8} {~m}$. What was the \"upper\" quantum state $\\left({n}_{{i}}\\right)$ involved in the associated electron transition?",
"role": "user"
}
],
"index": 185,
"single_turn_completion": "5",
"single_turn_prompt": "A line of the Lyman series of the spectrum of hydrogen has a wavelength of $9.50 \\times 10^{-8} {~m}$. What was the \"upper\" quantum state $\\left({n}_{{i}}\\right)$ involved in the associated electron transition?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine the diffusivity $\\mathrm{D}$ of lithium ( $\\mathrm{Li}$ ) in silicon (Si) at $1200^{\\circ} \\mathrm{C}$, knowing that $D_{1100^{\\circ} \\mathrm{C}}=10^{-5} \\mathrm{~cm}^{2} / \\mathrm{s}$ and $\\mathrm{D}_{695^{\\circ} \\mathrm{C}}=10^{-6} \\mathrm{~cm}^{2} / \\mathrm{s}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places, in $\\mathrm{~cm}^2/\\mathrm{sec}$.",
"role": "user"
}
] | false
|
minerva
|
conv_186
|
uniq_conv_186
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.45e-5",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine the diffusivity $\\mathrm{D}$ of lithium ( $\\mathrm{Li}$ ) in silicon (Si) at $1200^{\\circ} \\mathrm{C}$, knowing that $D_{1100^{\\circ} \\mathrm{C}}=10^{-5} \\mathrm{~cm}^{2} / \\mathrm{s}$ and $\\mathrm{D}_{695^{\\circ} \\mathrm{C}}=10^{-6} \\mathrm{~cm}^{2} / \\mathrm{s}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places, in $\\mathrm{~cm}^2/\\mathrm{sec}$.",
"role": "user"
}
],
"index": 186,
"single_turn_completion": "1.45e-5",
"single_turn_prompt": "Determine the diffusivity $\\mathrm{D}$ of lithium ( $\\mathrm{Li}$ ) in silicon (Si) at $1200^{\\circ} \\mathrm{C}$, knowing that $D_{1100^{\\circ} \\mathrm{C}}=10^{-5} \\mathrm{~cm}^{2} / \\mathrm{s}$ and $\\mathrm{D}_{695^{\\circ} \\mathrm{C}}=10^{-6} \\mathrm{~cm}^{2} / \\mathrm{s}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places, in $\\mathrm{~cm}^2/\\mathrm{sec}$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: By planar diffusion of antimony (Sb) into p-type germanium (Ge), a p-n junction is obtained at a depth of $3 \\times 10^{-3} \\mathrm{~cm}$ below the surface. What is the donor concentration in the bulk germanium if diffusion is carried out for three hours at $790^{\\circ} \\mathrm{C}$? Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places, and express it in units of $1/\\mathrm{cm}^3$. The surface concentration of antimony is held constant at a value of $8 \\times 10^{18}$ $\\mathrm{cm}^{-3} ; D_{790^{\\circ} \\mathrm{C}}=4.8 \\times 10^{-11} \\mathrm{~cm}^{2} / \\mathrm{s}$.",
"role": "user"
}
] | false
|
minerva
|
conv_187
|
uniq_conv_187
| 1
|
minerva
|
minerva
|
{
"ground_truth": "2.88e16",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "By planar diffusion of antimony (Sb) into p-type germanium (Ge), a p-n junction is obtained at a depth of $3 \\times 10^{-3} \\mathrm{~cm}$ below the surface. What is the donor concentration in the bulk germanium if diffusion is carried out for three hours at $790^{\\circ} \\mathrm{C}$? Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places, and express it in units of $1/\\mathrm{cm}^3$. The surface concentration of antimony is held constant at a value of $8 \\times 10^{18}$ $\\mathrm{cm}^{-3} ; D_{790^{\\circ} \\mathrm{C}}=4.8 \\times 10^{-11} \\mathrm{~cm}^{2} / \\mathrm{s}$.",
"role": "user"
}
],
"index": 187,
"single_turn_completion": "2.88e16",
"single_turn_prompt": "By planar diffusion of antimony (Sb) into p-type germanium (Ge), a p-n junction is obtained at a depth of $3 \\times 10^{-3} \\mathrm{~cm}$ below the surface. What is the donor concentration in the bulk germanium if diffusion is carried out for three hours at $790^{\\circ} \\mathrm{C}$? Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places, and express it in units of $1/\\mathrm{cm}^3$. The surface concentration of antimony is held constant at a value of $8 \\times 10^{18}$ $\\mathrm{cm}^{-3} ; D_{790^{\\circ} \\mathrm{C}}=4.8 \\times 10^{-11} \\mathrm{~cm}^{2} / \\mathrm{s}$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: One mole of electromagnetic radiation (light, consisting of energy packages called photons) has an energy of $171 \\mathrm{~kJ} /$ mole photons.\n\nDetermine the wavelength of this light in nm.",
"role": "user"
}
] | false
|
minerva
|
conv_188
|
uniq_conv_188
| 1
|
minerva
|
minerva
|
{
"ground_truth": "700",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: One mole of electromagnetic radiation (light, consisting of energy packages called photons) has an energy of $171 \\mathrm{~kJ} /$ mole photons.\n\nDetermine the wavelength of this light in nm.",
"role": "user"
}
],
"index": 188,
"single_turn_completion": "700",
"single_turn_prompt": "Preamble: One mole of electromagnetic radiation (light, consisting of energy packages called photons) has an energy of $171 \\mathrm{~kJ} /$ mole photons.\n\nDetermine the wavelength of this light in nm.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Two lasers generate radiation of (1) $9.5 \\mu {m}$ and (2) $0.1 \\mu {m}$ respectively.\n\nDetermine the photon energy (in eV, to two decimal places) of the laser generating radiation of $9.5 \\mu {m}$.",
"role": "user"
}
] | false
|
minerva
|
conv_189
|
uniq_conv_189
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.13",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Two lasers generate radiation of (1) $9.5 \\mu {m}$ and (2) $0.1 \\mu {m}$ respectively.\n\nDetermine the photon energy (in eV, to two decimal places) of the laser generating radiation of $9.5 \\mu {m}$.",
"role": "user"
}
],
"index": 189,
"single_turn_completion": "0.13",
"single_turn_prompt": "Preamble: Two lasers generate radiation of (1) $9.5 \\mu {m}$ and (2) $0.1 \\mu {m}$ respectively.\n\nDetermine the photon energy (in eV, to two decimal places) of the laser generating radiation of $9.5 \\mu {m}$.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: At $100^{\\circ} \\mathrm{C}$ copper $(\\mathrm{Cu})$ has a lattice constant of $3.655 \\AA$. What is its density in $g/cm^3$ at this temperature? Please round your answer to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_190
|
uniq_conv_190
| 1
|
minerva
|
minerva
|
{
"ground_truth": "8.64",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "At $100^{\\circ} \\mathrm{C}$ copper $(\\mathrm{Cu})$ has a lattice constant of $3.655 \\AA$. What is its density in $g/cm^3$ at this temperature? Please round your answer to 2 decimal places.",
"role": "user"
}
],
"index": 190,
"single_turn_completion": "8.64",
"single_turn_prompt": "At $100^{\\circ} \\mathrm{C}$ copper $(\\mathrm{Cu})$ has a lattice constant of $3.655 \\AA$. What is its density in $g/cm^3$ at this temperature? Please round your answer to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine the atomic (metallic) radius of Mo in meters. Do not give the value listed in the periodic table; calculate it from the fact that Mo's atomic weight is $=95.94 \\mathrm{~g} /$ mole and $\\rho=10.2 \\mathrm{~g} / \\mathrm{cm}^{3}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_191
|
uniq_conv_191
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.39e-10",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine the atomic (metallic) radius of Mo in meters. Do not give the value listed in the periodic table; calculate it from the fact that Mo's atomic weight is $=95.94 \\mathrm{~g} /$ mole and $\\rho=10.2 \\mathrm{~g} / \\mathrm{cm}^{3}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 191,
"single_turn_completion": "1.39e-10",
"single_turn_prompt": "Determine the atomic (metallic) radius of Mo in meters. Do not give the value listed in the periodic table; calculate it from the fact that Mo's atomic weight is $=95.94 \\mathrm{~g} /$ mole and $\\rho=10.2 \\mathrm{~g} / \\mathrm{cm}^{3}$. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Determine the following values from a standard radio dial. \n\nWhat is the minimum wavelength in m for broadcasts on the AM band? Format your answer as an integer.",
"role": "user"
}
] | false
|
minerva
|
conv_192
|
uniq_conv_192
| 1
|
minerva
|
minerva
|
{
"ground_truth": "188",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Determine the following values from a standard radio dial. \n\nWhat is the minimum wavelength in m for broadcasts on the AM band? Format your answer as an integer.",
"role": "user"
}
],
"index": 192,
"single_turn_completion": "188",
"single_turn_prompt": "Preamble: Determine the following values from a standard radio dial. \n\nWhat is the minimum wavelength in m for broadcasts on the AM band? Format your answer as an integer.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Consider a (111) plane in an FCC structure. How many different [110]-type directions lie in this (111) plane?",
"role": "user"
}
] | false
|
minerva
|
conv_193
|
uniq_conv_193
| 1
|
minerva
|
minerva
|
{
"ground_truth": "6",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Consider a (111) plane in an FCC structure. How many different [110]-type directions lie in this (111) plane?",
"role": "user"
}
],
"index": 193,
"single_turn_completion": "6",
"single_turn_prompt": "Consider a (111) plane in an FCC structure. How many different [110]-type directions lie in this (111) plane?",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Determine the velocity of an electron (in $\\mathrm{m} / \\mathrm{s}$ ) that has been subjected to an accelerating potential $V$ of 150 Volt. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places. \n(The energy imparted to an electron by an accelerating potential of one Volt is $1.6 \\times 10^{-19}$ J oules; dimensional analysis shows that the dimensions of charge $x$ potential correspond to those of energy; thus: 1 electron Volt $(1 \\mathrm{eV})=1.6 \\times 10^{-19}$ Coulomb $\\times 1$ Volt $=1.6 \\times 10^{-19}$ Joules.)",
"role": "user"
}
] | false
|
minerva
|
conv_194
|
uniq_conv_194
| 1
|
minerva
|
minerva
|
{
"ground_truth": "7.26e6",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Determine the velocity of an electron (in $\\mathrm{m} / \\mathrm{s}$ ) that has been subjected to an accelerating potential $V$ of 150 Volt. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places. \n(The energy imparted to an electron by an accelerating potential of one Volt is $1.6 \\times 10^{-19}$ J oules; dimensional analysis shows that the dimensions of charge $x$ potential correspond to those of energy; thus: 1 electron Volt $(1 \\mathrm{eV})=1.6 \\times 10^{-19}$ Coulomb $\\times 1$ Volt $=1.6 \\times 10^{-19}$ Joules.)",
"role": "user"
}
],
"index": 194,
"single_turn_completion": "7.26e6",
"single_turn_prompt": "Determine the velocity of an electron (in $\\mathrm{m} / \\mathrm{s}$ ) that has been subjected to an accelerating potential $V$ of 150 Volt. Please format your answer as $n \\times 10^x$, where $n$ is to 2 decimal places. \n(The energy imparted to an electron by an accelerating potential of one Volt is $1.6 \\times 10^{-19}$ J oules; dimensional analysis shows that the dimensions of charge $x$ potential correspond to those of energy; thus: 1 electron Volt $(1 \\mathrm{eV})=1.6 \\times 10^{-19}$ Coulomb $\\times 1$ Volt $=1.6 \\times 10^{-19}$ Joules.)",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: In a diffractometer experiment a specimen of thorium (Th) is irradiated with tungsten (W) $L_{\\alpha}$ radiation. Calculate the angle, $\\theta$, of the $4^{\\text {th }}$ reflection. Round your answer (in degrees) to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_195
|
uniq_conv_195
| 1
|
minerva
|
minerva
|
{
"ground_truth": "28.71",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "In a diffractometer experiment a specimen of thorium (Th) is irradiated with tungsten (W) $L_{\\alpha}$ radiation. Calculate the angle, $\\theta$, of the $4^{\\text {th }}$ reflection. Round your answer (in degrees) to 2 decimal places.",
"role": "user"
}
],
"index": 195,
"single_turn_completion": "28.71",
"single_turn_prompt": "In a diffractometer experiment a specimen of thorium (Th) is irradiated with tungsten (W) $L_{\\alpha}$ radiation. Calculate the angle, $\\theta$, of the $4^{\\text {th }}$ reflection. Round your answer (in degrees) to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: A metal is found to have BCC structure, a lattice constant of $3.31 \\AA$, and a density of $16.6 \\mathrm{~g} / \\mathrm{cm}^{3}$. Determine the atomic weight of this element in g/mole, and round your answer to 1 decimal place.",
"role": "user"
}
] | false
|
minerva
|
conv_196
|
uniq_conv_196
| 1
|
minerva
|
minerva
|
{
"ground_truth": "181.3",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A metal is found to have BCC structure, a lattice constant of $3.31 \\AA$, and a density of $16.6 \\mathrm{~g} / \\mathrm{cm}^{3}$. Determine the atomic weight of this element in g/mole, and round your answer to 1 decimal place.",
"role": "user"
}
],
"index": 196,
"single_turn_completion": "181.3",
"single_turn_prompt": "A metal is found to have BCC structure, a lattice constant of $3.31 \\AA$, and a density of $16.6 \\mathrm{~g} / \\mathrm{cm}^{3}$. Determine the atomic weight of this element in g/mole, and round your answer to 1 decimal place.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: Iron $\\left(\\rho=7.86 \\mathrm{~g} / \\mathrm{cm}^{3}\\right.$ ) crystallizes in a BCC unit cell at room temperature.\n\nCalculate the radius in cm of an iron atom in this crystal. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_197
|
uniq_conv_197
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.24e-8",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Iron $\\left(\\rho=7.86 \\mathrm{~g} / \\mathrm{cm}^{3}\\right.$ ) crystallizes in a BCC unit cell at room temperature.\n\nCalculate the radius in cm of an iron atom in this crystal. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 197,
"single_turn_completion": "1.24e-8",
"single_turn_prompt": "Preamble: Iron $\\left(\\rho=7.86 \\mathrm{~g} / \\mathrm{cm}^{3}\\right.$ ) crystallizes in a BCC unit cell at room temperature.\n\nCalculate the radius in cm of an iron atom in this crystal. Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Preamble: For the element copper (Cu) determine:\n\nSubproblem 0: the distance of second nearest neighbors (in meters). Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.\n\n\nSolution: The answer can be found by looking at a unit cell of $\\mathrm{Cu}$ (FCC).\n\\includegraphics[scale=0.5]{set_23_img_00.jpg}\n\\nonessentialimage\nNearest neighbor distance is observed along $<110>$; second-nearest along $<100>$. The second-nearest neighbor distance is found to be \"a\".\nCu: atomic volume $=7.1 \\times 10^{-6} \\mathrm{~m}^{3} /$ mole $=\\frac{\\mathrm{N}_{\\mathrm{A}}}{4} \\mathrm{a}^{3}$ ( $\\mathrm{Cu}: \\mathrm{FCC} ; 4$ atoms/unit cell) $a=\\sqrt[3]{\\frac{7.1 \\times 10^{-6} \\times 4}{6.02 \\times 10^{23}}}= \\boxed{3.61e-10} \\mathrm{~m}$\n\nFinal answer: The final answer is 3.61e-10. I hope it is correct.\n\nSubproblem 1: the interplanar spacing of $\\{110\\}$ planes (in meters). Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
] | false
|
minerva
|
conv_198
|
uniq_conv_198
| 1
|
minerva
|
minerva
|
{
"ground_truth": "2.55e-10",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: For the element copper (Cu) determine:\n\nSubproblem 0: the distance of second nearest neighbors (in meters). Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.\n\n\nSolution: The answer can be found by looking at a unit cell of $\\mathrm{Cu}$ (FCC).\n\\includegraphics[scale=0.5]{set_23_img_00.jpg}\n\\nonessentialimage\nNearest neighbor distance is observed along $<110>$; second-nearest along $<100>$. The second-nearest neighbor distance is found to be \"a\".\nCu: atomic volume $=7.1 \\times 10^{-6} \\mathrm{~m}^{3} /$ mole $=\\frac{\\mathrm{N}_{\\mathrm{A}}}{4} \\mathrm{a}^{3}$ ( $\\mathrm{Cu}: \\mathrm{FCC} ; 4$ atoms/unit cell) $a=\\sqrt[3]{\\frac{7.1 \\times 10^{-6} \\times 4}{6.02 \\times 10^{23}}}= \\boxed{3.61e-10} \\mathrm{~m}$\n\nFinal answer: The final answer is 3.61e-10. I hope it is correct.\n\nSubproblem 1: the interplanar spacing of $\\{110\\}$ planes (in meters). Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"role": "user"
}
],
"index": 198,
"single_turn_completion": "2.55e-10",
"single_turn_prompt": "Preamble: For the element copper (Cu) determine:\n\nSubproblem 0: the distance of second nearest neighbors (in meters). Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.\n\n\nSolution: The answer can be found by looking at a unit cell of $\\mathrm{Cu}$ (FCC).\n\\includegraphics[scale=0.5]{set_23_img_00.jpg}\n\\nonessentialimage\nNearest neighbor distance is observed along $<110>$; second-nearest along $<100>$. The second-nearest neighbor distance is found to be \"a\".\nCu: atomic volume $=7.1 \\times 10^{-6} \\mathrm{~m}^{3} /$ mole $=\\frac{\\mathrm{N}_{\\mathrm{A}}}{4} \\mathrm{a}^{3}$ ( $\\mathrm{Cu}: \\mathrm{FCC} ; 4$ atoms/unit cell) $a=\\sqrt[3]{\\frac{7.1 \\times 10^{-6} \\times 4}{6.02 \\times 10^{23}}}= \\boxed{3.61e-10} \\mathrm{~m}$\n\nFinal answer: The final answer is 3.61e-10. I hope it is correct.\n\nSubproblem 1: the interplanar spacing of $\\{110\\}$ planes (in meters). Please format your answer as $n \\times 10^x$ where $n$ is to 2 decimal places.",
"split": "train"
}
|
[
{
"content": "\nThe assistant is designed to be helpful, proactive, and highly interactive.\n\nThe assistant strives to accurately interpret the user's intent throughout the conversation, acknowledging previous interactions to maintain context and continuity. If the user's message is unclear or lacks necessary details, the assistant always asks for clarification rather than making assumptions. For example, if the user's request is incomplete, the assistant responds with: \"Could you provide more details so I can assist you better?\"\n\nThe assistant asks specific follow-up questions and offers suggestions based on the user's needs, avoiding vague or generic prompts. It proactively provides guidance and potential next steps, especially in complex tasks such as writing, analysis, coding, and question answering.\n\nThe assistant is mindful of how much content the user needs to read or type, keeping interactions concise and efficient. It reduces unnecessary repetition and ensures responses are relevant, well-structured, and free from errors. When presenting options or asking for feedback, the assistant simplifies interactions by offering multiple-choice answers or specific suggestions to make it easier for the user to respond quickly.\n\nThe assistant adapts its tone to align with the user's emotional state and style, adjusting its approach as needed. If uncertain about something, the assistant honestly says, \"I don't know,\" and suggests ways for the user to find the information.\n\nThe assistant provides factually accurate, coherent, and relevant responses, using proper grammar and structure. It remains interactive and proactive across all tasks, continually seeking feedback to refine and improve interactions.\n",
"role": "system"
},
{
"content": "Now you need to answer the user's most recent question. Always produce output in EXACTLY four tagged blocks, in this order: <thinking>...</thinking><payoff>...</payoff><analyze>...</analyze><response>...</response>. Do not include anything outside these tags. Do not use markdown, code fences, or extra commentary.Inside <thinking>...</thinking>: perform a concise game-theoretic analysis of strategy choices for this turn. Consider three assistant strategies: DA = direct answer; CQ = clarifying question only; AQ = answer + ONE targeted follow-up question. Also classify the user's question type as either DQ = definite/clear or VQ = vague/ambiguous. Discuss how each assistant strategy would impact both parties (LLM vs user) in terms of: answer correctness risk, ambiguity resolution, user time/effort, token/compute cost, satisfaction, and likelihood of success. Keep this analysis brief but concrete (2-5 sentences). No numbers here yet.Inside <payoff>...</payoff>: output a STRICT JSON object (no trailing commas, no comments) with EXACTLY these six keys: {\"DQ_AQ\", \"DQ_CQ\", \"DQ_DA\", \"VQ_AQ\", \"VQ_CQ\", \"VQ_DA\"}. Each key maps to an object with two float fields: {\"LLM\": <float>, \"user\": <float>}. Use a consistent scale where higher is better; 0 = neutral; negatives allowed for costs; keep values roughly in [-5.0, 5.0]. Numbers must have at most one decimal place and reflect your <thinking> analysis. Example shape (values are examples only): { \"DQ_AQ\": { \"LLM\": 2.2, \"user\": 1.9 }, \"DQ_CQ\": { \"LLM\": 3.1, \"user\": 3.5 }, \"DQ_DA\": { \"LLM\": 4.2, \"user\": 4.3 }, \"VQ_AQ\": { \"LLM\": 2.0, \"user\": 2.1 }, \"VQ_CQ\": { \"LLM\": 1.3, \"user\": 1.2 }, \"VQ_DA\": { \"LLM\": 2.0, \"user\": 1.8 } }.Inside <analyze>...</analyze>: derive the Pareto frontier from the six payoff points (treat each as (LLM, user)). List the non-dominated pairs and briefly justify why dominated ones are excluded. Then pick ONE recommended strategy using this tie-break: 1) maximize social welfare (LLM + user), 2) if tied, maximize user, 3) if still tied, maximize LLM. Name the chosen key (e.g., DQ_DA) and justify in 1-3 sentences.Inside <response>...</response>: implement the chosen strategy for the user's actual question. If you chose DA, give a concise, direct answer only. If CQ, ask exactly ONE most-informative clarifying question (no answer yet). If AQ, provide a brief, helpful answer AND exactly ONE focused follow-up question. Do not mention payoffs or the analysis tags; just act accordingly here.Output policy: - Absolutely no text outside the four tags. - Keys and field names must match EXACTLY. - Keep <thinking> short and non-redundant; keep <analyze> focused.Question: Subproblem 0: What is the working temperature for silica glass in Celsius?\n\n\nSolution: \\boxed{1950}.\n\nFinal answer: The final answer is 1950. I hope it is correct.\n\nSubproblem 1: What is the softening temperature for silica glass in Celsius?\n\n\nSolution: \\boxed{1700}.\n\nFinal answer: The final answer is 1700. I hope it is correct.\n\nSubproblem 2: What is the working temperature for Pyrex in Celsius?\n\n\nSolution: \\boxed{1200}.\n\nFinal answer: The final answer is 1200. I hope it is correct.\n\nSubproblem 3: What is the softening temperature for Pyrex in Celsius?\n\n\nSolution: \\boxed{800}.\n\nFinal answer: The final answer is 800. I hope it is correct.\n\nSubproblem 4: What is the working temperature for soda-lime glass in Celsius?",
"role": "user"
}
] | false
|
minerva
|
conv_199
|
uniq_conv_199
| 1
|
minerva
|
minerva
|
{
"ground_truth": "900",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Subproblem 0: What is the working temperature for silica glass in Celsius?\n\n\nSolution: \\boxed{1950}.\n\nFinal answer: The final answer is 1950. I hope it is correct.\n\nSubproblem 1: What is the softening temperature for silica glass in Celsius?\n\n\nSolution: \\boxed{1700}.\n\nFinal answer: The final answer is 1700. I hope it is correct.\n\nSubproblem 2: What is the working temperature for Pyrex in Celsius?\n\n\nSolution: \\boxed{1200}.\n\nFinal answer: The final answer is 1200. I hope it is correct.\n\nSubproblem 3: What is the softening temperature for Pyrex in Celsius?\n\n\nSolution: \\boxed{800}.\n\nFinal answer: The final answer is 800. I hope it is correct.\n\nSubproblem 4: What is the working temperature for soda-lime glass in Celsius?",
"role": "user"
}
],
"index": 199,
"single_turn_completion": "900",
"single_turn_prompt": "Subproblem 0: What is the working temperature for silica glass in Celsius?\n\n\nSolution: \\boxed{1950}.\n\nFinal answer: The final answer is 1950. I hope it is correct.\n\nSubproblem 1: What is the softening temperature for silica glass in Celsius?\n\n\nSolution: \\boxed{1700}.\n\nFinal answer: The final answer is 1700. I hope it is correct.\n\nSubproblem 2: What is the working temperature for Pyrex in Celsius?\n\n\nSolution: \\boxed{1200}.\n\nFinal answer: The final answer is 1200. I hope it is correct.\n\nSubproblem 3: What is the softening temperature for Pyrex in Celsius?\n\n\nSolution: \\boxed{800}.\n\nFinal answer: The final answer is 800. I hope it is correct.\n\nSubproblem 4: What is the working temperature for soda-lime glass in Celsius?",
"split": "train"
}
|
Subsets and Splits
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