Dataset Viewer
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: Each of the two Magellan telescopes has a diameter of $6.5 \\mathrm{~m}$. In one configuration the effective focal length is $72 \\mathrm{~m}$. Find the diameter of the image of a planet (in $\\mathrm{cm}$ ) at this focus if the angular diameter of the planet at the time of the observation is $45^{\\prime \\prime}$.",
"role": "user"
}
] | false
|
minerva
|
conv_0
|
uniq_conv_0
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.6",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Each of the two Magellan telescopes has a diameter of $6.5 \\mathrm{~m}$. In one configuration the effective focal length is $72 \\mathrm{~m}$. Find the diameter of the image of a planet (in $\\mathrm{cm}$ ) at this focus if the angular diameter of the planet at the time of the observation is $45^{\\prime \\prime}$.",
"role": "user"
}
],
"index": 0,
"single_turn_completion": "1.6",
"single_turn_prompt": "Each of the two Magellan telescopes has a diameter of $6.5 \\mathrm{~m}$. In one configuration the effective focal length is $72 \\mathrm{~m}$. Find the diameter of the image of a planet (in $\\mathrm{cm}$ ) at this focus if the angular diameter of the planet at the time of the observation is $45^{\\prime \\prime}$.",
"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 white dwarf star has an effective temperature, $T_{e}=50,000$ degrees Kelvin, but its radius, $R_{\\mathrm{WD}}$, is comparable to that of the Earth. Take $R_{\\mathrm{WD}}=10^{4} \\mathrm{~km}\\left(10^{7} \\mathrm{~m}\\right.$ or $\\left.10^{9} \\mathrm{~cm}\\right)$. Compute the luminosity (power output) of the white dwarf. Treat the white dwarf as a blackbody radiator. Give your answer in units of ergs per second, to two significant figures.",
"role": "user"
}
] | false
|
minerva
|
conv_1
|
uniq_conv_1
| 1
|
minerva
|
minerva
|
{
"ground_truth": "4.5e33",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A white dwarf star has an effective temperature, $T_{e}=50,000$ degrees Kelvin, but its radius, $R_{\\mathrm{WD}}$, is comparable to that of the Earth. Take $R_{\\mathrm{WD}}=10^{4} \\mathrm{~km}\\left(10^{7} \\mathrm{~m}\\right.$ or $\\left.10^{9} \\mathrm{~cm}\\right)$. Compute the luminosity (power output) of the white dwarf. Treat the white dwarf as a blackbody radiator. Give your answer in units of ergs per second, to two significant figures.",
"role": "user"
}
],
"index": 1,
"single_turn_completion": "4.5e33",
"single_turn_prompt": "A white dwarf star has an effective temperature, $T_{e}=50,000$ degrees Kelvin, but its radius, $R_{\\mathrm{WD}}$, is comparable to that of the Earth. Take $R_{\\mathrm{WD}}=10^{4} \\mathrm{~km}\\left(10^{7} \\mathrm{~m}\\right.$ or $\\left.10^{9} \\mathrm{~cm}\\right)$. Compute the luminosity (power output) of the white dwarf. Treat the white dwarf as a blackbody radiator. Give your answer in units of ergs per second, to two 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: A prism is constructed from glass and has sides that form a right triangle with the other two angles equal to $45^{\\circ}$. The sides are $L, L$, and $H$, where $L$ is a leg and $H$ is the hypotenuse. A parallel light beam enters side $L$ normal to the surface, passes into the glass, and then strikes $H$ internally. The index of refraction of the glass is $n=1.5$.\n\nCompute the critical angle for the light to be internally reflected at $H$. Give your answer in degrees to 3 significant figures.",
"role": "user"
}
] | false
|
minerva
|
conv_2
|
uniq_conv_2
| 1
|
minerva
|
minerva
|
{
"ground_truth": "41.8",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: A prism is constructed from glass and has sides that form a right triangle with the other two angles equal to $45^{\\circ}$. The sides are $L, L$, and $H$, where $L$ is a leg and $H$ is the hypotenuse. A parallel light beam enters side $L$ normal to the surface, passes into the glass, and then strikes $H$ internally. The index of refraction of the glass is $n=1.5$.\n\nCompute the critical angle for the light to be internally reflected at $H$. Give your answer in degrees to 3 significant figures.",
"role": "user"
}
],
"index": 2,
"single_turn_completion": "41.8",
"single_turn_prompt": "Preamble: A prism is constructed from glass and has sides that form a right triangle with the other two angles equal to $45^{\\circ}$. The sides are $L, L$, and $H$, where $L$ is a leg and $H$ is the hypotenuse. A parallel light beam enters side $L$ normal to the surface, passes into the glass, and then strikes $H$ internally. The index of refraction of the glass is $n=1.5$.\n\nCompute the critical angle for the light to be internally reflected at $H$. Give your answer in degrees to 3 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: A particular star has an absolute magnitude $M=-7$. If this star is observed in a galaxy that is at a distance of $3 \\mathrm{Mpc}$, what will its apparent magnitude be?",
"role": "user"
}
] | false
|
minerva
|
conv_3
|
uniq_conv_3
| 1
|
minerva
|
minerva
|
{
"ground_truth": "20.39",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A particular star has an absolute magnitude $M=-7$. If this star is observed in a galaxy that is at a distance of $3 \\mathrm{Mpc}$, what will its apparent magnitude be?",
"role": "user"
}
],
"index": 3,
"single_turn_completion": "20.39",
"single_turn_prompt": "A particular star has an absolute magnitude $M=-7$. If this star is observed in a galaxy that is at a distance of $3 \\mathrm{Mpc}$, what will its apparent magnitude be?",
"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 gravitational acceleration due to the Sun at the location of the Earth's orbit (i.e., at a distance of $1 \\mathrm{AU}$ ). Give your answer in meters per second squared, and express it to one significant figure.",
"role": "user"
}
] | false
|
minerva
|
conv_4
|
uniq_conv_4
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.006",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Find the gravitational acceleration due to the Sun at the location of the Earth's orbit (i.e., at a distance of $1 \\mathrm{AU}$ ). Give your answer in meters per second squared, and express it to one significant figure.",
"role": "user"
}
],
"index": 4,
"single_turn_completion": "0.006",
"single_turn_prompt": "Find the gravitational acceleration due to the Sun at the location of the Earth's orbit (i.e., at a distance of $1 \\mathrm{AU}$ ). Give your answer in meters per second squared, and express it to one significant figure.",
"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 collimated light beam propagating in water is incident on the surface (air/water interface) at an angle $\\theta_w$ with respect to the surface normal.\n\nSubproblem 0: If the index of refraction of water is $n=1.3$, find an expression for the angle of the light once it emerges from the water into the air, $\\theta_a$, in terms of $\\theta_w$.\n\n\nSolution: Using Snell's law, $1.3 \\sin{\\theta_w} = \\sin{\\theta_a}$. So $\\theta_a = \\boxed{\\arcsin{1.3 \\sin{\\theta_w}}}$.\n\nFinal answer: The final answer is \\arcsin{1.3 \\sin{\\theta_w}}. I hope it is correct.\n\nSubproblem 1: What is the critical angle, i.e., the critical value of $\\theta_w$ such that the light will not emerge from the water? Leave your answer in terms of inverse trigonometric functions; i.e., do not evaluate the function.",
"role": "user"
}
] | false
|
minerva
|
conv_5
|
uniq_conv_5
| 1
|
minerva
|
minerva
|
{
"ground_truth": "np.arcsin(10/13)",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: A collimated light beam propagating in water is incident on the surface (air/water interface) at an angle $\\theta_w$ with respect to the surface normal.\n\nSubproblem 0: If the index of refraction of water is $n=1.3$, find an expression for the angle of the light once it emerges from the water into the air, $\\theta_a$, in terms of $\\theta_w$.\n\n\nSolution: Using Snell's law, $1.3 \\sin{\\theta_w} = \\sin{\\theta_a}$. So $\\theta_a = \\boxed{\\arcsin{1.3 \\sin{\\theta_w}}}$.\n\nFinal answer: The final answer is \\arcsin{1.3 \\sin{\\theta_w}}. I hope it is correct.\n\nSubproblem 1: What is the critical angle, i.e., the critical value of $\\theta_w$ such that the light will not emerge from the water? Leave your answer in terms of inverse trigonometric functions; i.e., do not evaluate the function.",
"role": "user"
}
],
"index": 5,
"single_turn_completion": "np.arcsin(10/13)",
"single_turn_prompt": "Preamble: A collimated light beam propagating in water is incident on the surface (air/water interface) at an angle $\\theta_w$ with respect to the surface normal.\n\nSubproblem 0: If the index of refraction of water is $n=1.3$, find an expression for the angle of the light once it emerges from the water into the air, $\\theta_a$, in terms of $\\theta_w$.\n\n\nSolution: Using Snell's law, $1.3 \\sin{\\theta_w} = \\sin{\\theta_a}$. So $\\theta_a = \\boxed{\\arcsin{1.3 \\sin{\\theta_w}}}$.\n\nFinal answer: The final answer is \\arcsin{1.3 \\sin{\\theta_w}}. I hope it is correct.\n\nSubproblem 1: What is the critical angle, i.e., the critical value of $\\theta_w$ such that the light will not emerge from the water? Leave your answer in terms of inverse trigonometric functions; i.e., do not evaluate the 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: Find the theoretical limiting angular resolution (in arcsec) of a commercial 8-inch (diameter) optical telescope being used in the visible spectrum (at $\\lambda=5000 \\AA=500 \\mathrm{~nm}=5 \\times 10^{-5} \\mathrm{~cm}=5 \\times 10^{-7} \\mathrm{~m}$). Answer in arcseconds to two significant figures.",
"role": "user"
}
] | false
|
minerva
|
conv_6
|
uniq_conv_6
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.49",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Find the theoretical limiting angular resolution (in arcsec) of a commercial 8-inch (diameter) optical telescope being used in the visible spectrum (at $\\lambda=5000 \\AA=500 \\mathrm{~nm}=5 \\times 10^{-5} \\mathrm{~cm}=5 \\times 10^{-7} \\mathrm{~m}$). Answer in arcseconds to two significant figures.",
"role": "user"
}
],
"index": 6,
"single_turn_completion": "0.49",
"single_turn_prompt": "Find the theoretical limiting angular resolution (in arcsec) of a commercial 8-inch (diameter) optical telescope being used in the visible spectrum (at $\\lambda=5000 \\AA=500 \\mathrm{~nm}=5 \\times 10^{-5} \\mathrm{~cm}=5 \\times 10^{-7} \\mathrm{~m}$). Answer in arcseconds to two 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: A star has a measured parallax of $0.01^{\\prime \\prime}$, that is, $0.01$ arcseconds. How far away is it, in parsecs?",
"role": "user"
}
] | false
|
minerva
|
conv_7
|
uniq_conv_7
| 1
|
minerva
|
minerva
|
{
"ground_truth": "100",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A star has a measured parallax of $0.01^{\\prime \\prime}$, that is, $0.01$ arcseconds. How far away is it, in parsecs?",
"role": "user"
}
],
"index": 7,
"single_turn_completion": "100",
"single_turn_prompt": "A star has a measured parallax of $0.01^{\\prime \\prime}$, that is, $0.01$ arcseconds. How far away is it, in parsecs?",
"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: An extrasolar planet has been observed which passes in front of (i.e., transits) its parent star. If the planet is dark (i.e., contributes essentially no light of its own) and has a surface area that is $2 \\%$ of that of its parent star, find the decrease in magnitude of the system during transits.",
"role": "user"
}
] | false
|
minerva
|
conv_8
|
uniq_conv_8
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.022",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "An extrasolar planet has been observed which passes in front of (i.e., transits) its parent star. If the planet is dark (i.e., contributes essentially no light of its own) and has a surface area that is $2 \\%$ of that of its parent star, find the decrease in magnitude of the system during transits.",
"role": "user"
}
],
"index": 8,
"single_turn_completion": "0.022",
"single_turn_prompt": "An extrasolar planet has been observed which passes in front of (i.e., transits) its parent star. If the planet is dark (i.e., contributes essentially no light of its own) and has a surface area that is $2 \\%$ of that of its parent star, find the decrease in magnitude of the system during transits.",
"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: If the Bohr energy levels scale as $Z^{2}$, where $Z$ is the atomic number of the atom (i.e., the charge on the nucleus), estimate the wavelength of a photon that results from a transition from $n=3$ to $n=2$ in Fe, which has $Z=26$. Assume that the Fe atom is completely stripped of all its electrons except for one. Give your answer in Angstroms, to two significant figures.",
"role": "user"
}
] | false
|
minerva
|
conv_9
|
uniq_conv_9
| 1
|
minerva
|
minerva
|
{
"ground_truth": "9.6",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "If the Bohr energy levels scale as $Z^{2}$, where $Z$ is the atomic number of the atom (i.e., the charge on the nucleus), estimate the wavelength of a photon that results from a transition from $n=3$ to $n=2$ in Fe, which has $Z=26$. Assume that the Fe atom is completely stripped of all its electrons except for one. Give your answer in Angstroms, to two significant figures.",
"role": "user"
}
],
"index": 9,
"single_turn_completion": "9.6",
"single_turn_prompt": "If the Bohr energy levels scale as $Z^{2}$, where $Z$ is the atomic number of the atom (i.e., the charge on the nucleus), estimate the wavelength of a photon that results from a transition from $n=3$ to $n=2$ in Fe, which has $Z=26$. Assume that the Fe atom is completely stripped of all its electrons except for one. Give your answer in Angstroms, to two 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: If the Sun's absolute magnitude is $+5$, find the luminosity of a star of magnitude $0$ in ergs/s. A useful constant: the luminosity of the sun is $3.83 \\times 10^{33}$ ergs/s.",
"role": "user"
}
] | false
|
minerva
|
conv_10
|
uniq_conv_10
| 1
|
minerva
|
minerva
|
{
"ground_truth": "3.83e35",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "If the Sun's absolute magnitude is $+5$, find the luminosity of a star of magnitude $0$ in ergs/s. A useful constant: the luminosity of the sun is $3.83 \\times 10^{33}$ ergs/s.",
"role": "user"
}
],
"index": 10,
"single_turn_completion": "3.83e35",
"single_turn_prompt": "If the Sun's absolute magnitude is $+5$, find the luminosity of a star of magnitude $0$ in ergs/s. A useful constant: the luminosity of the sun is $3.83 \\times 10^{33}$ ergs/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: A spectrum is taken of a single star (i.e., one not in a binary). Among the observed spectral lines is one from oxygen whose rest wavelength is $5007 \\AA$. The Doppler shifted oxygen line from this star is observed to be at a wavelength of $5012 \\AA$. The star is also observed to have a proper motion, $\\mu$, of 1 arc second per year (which corresponds to $\\sim 1.5 \\times 10^{-13}$ radians per second of time). It is located at a distance of $60 \\mathrm{pc}$ from the Earth. Take the speed of light to be $3 \\times 10^8$ meters per second.\n\nWhat is the component of the star's velocity parallel to its vector to the Earth (in kilometers per second)?",
"role": "user"
}
] | false
|
minerva
|
conv_11
|
uniq_conv_11
| 1
|
minerva
|
minerva
|
{
"ground_truth": "300",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: A spectrum is taken of a single star (i.e., one not in a binary). Among the observed spectral lines is one from oxygen whose rest wavelength is $5007 \\AA$. The Doppler shifted oxygen line from this star is observed to be at a wavelength of $5012 \\AA$. The star is also observed to have a proper motion, $\\mu$, of 1 arc second per year (which corresponds to $\\sim 1.5 \\times 10^{-13}$ radians per second of time). It is located at a distance of $60 \\mathrm{pc}$ from the Earth. Take the speed of light to be $3 \\times 10^8$ meters per second.\n\nWhat is the component of the star's velocity parallel to its vector to the Earth (in kilometers per second)?",
"role": "user"
}
],
"index": 11,
"single_turn_completion": "300",
"single_turn_prompt": "Preamble: A spectrum is taken of a single star (i.e., one not in a binary). Among the observed spectral lines is one from oxygen whose rest wavelength is $5007 \\AA$. The Doppler shifted oxygen line from this star is observed to be at a wavelength of $5012 \\AA$. The star is also observed to have a proper motion, $\\mu$, of 1 arc second per year (which corresponds to $\\sim 1.5 \\times 10^{-13}$ radians per second of time). It is located at a distance of $60 \\mathrm{pc}$ from the Earth. Take the speed of light to be $3 \\times 10^8$ meters per second.\n\nWhat is the component of the star's velocity parallel to its vector to the Earth (in kilometers 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: The differential luminosity from a star, $\\Delta L$, with an approximate blackbody spectrum, is given by:\n\\[\n\\Delta L=\\frac{8 \\pi^{2} c^{2} R^{2}}{\\lambda^{5}\\left[e^{h c /(\\lambda k T)}-1\\right]} \\Delta \\lambda\n\\]\nwhere $R$ is the radius of the star, $T$ is its effective surface temperature, and $\\lambda$ is the wavelength. $\\Delta L$ is the power emitted by the star between wavelengths $\\lambda$ and $\\lambda+\\Delta \\lambda$ (assume $\\Delta \\lambda \\ll \\lambda)$. The star is at distance $d$. Find the star's spectral intensity $I(\\lambda)$ at the Earth, where $I(\\lambda)$ is defined as the power per unit area per unit wavelength interval.",
"role": "user"
}
] | false
|
minerva
|
conv_12
|
uniq_conv_12
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{2 \\pi c^{2} R^{2}}{\\lambda^{5}\\left[e^{h c /(\\lambda k T)}-1\\right] d^{2}}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "The differential luminosity from a star, $\\Delta L$, with an approximate blackbody spectrum, is given by:\n\\[\n\\Delta L=\\frac{8 \\pi^{2} c^{2} R^{2}}{\\lambda^{5}\\left[e^{h c /(\\lambda k T)}-1\\right]} \\Delta \\lambda\n\\]\nwhere $R$ is the radius of the star, $T$ is its effective surface temperature, and $\\lambda$ is the wavelength. $\\Delta L$ is the power emitted by the star between wavelengths $\\lambda$ and $\\lambda+\\Delta \\lambda$ (assume $\\Delta \\lambda \\ll \\lambda)$. The star is at distance $d$. Find the star's spectral intensity $I(\\lambda)$ at the Earth, where $I(\\lambda)$ is defined as the power per unit area per unit wavelength interval.",
"role": "user"
}
],
"index": 12,
"single_turn_completion": "\\frac{2 \\pi c^{2} R^{2}}{\\lambda^{5}\\left[e^{h c /(\\lambda k T)}-1\\right] d^{2}}",
"single_turn_prompt": "The differential luminosity from a star, $\\Delta L$, with an approximate blackbody spectrum, is given by:\n\\[\n\\Delta L=\\frac{8 \\pi^{2} c^{2} R^{2}}{\\lambda^{5}\\left[e^{h c /(\\lambda k T)}-1\\right]} \\Delta \\lambda\n\\]\nwhere $R$ is the radius of the star, $T$ is its effective surface temperature, and $\\lambda$ is the wavelength. $\\Delta L$ is the power emitted by the star between wavelengths $\\lambda$ and $\\lambda+\\Delta \\lambda$ (assume $\\Delta \\lambda \\ll \\lambda)$. The star is at distance $d$. Find the star's spectral intensity $I(\\lambda)$ at the Earth, where $I(\\lambda)$ is defined as the power per unit area per unit wavelength interval.",
"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 very hot star is detected in the galaxy M31 located at a distance of $800 \\mathrm{kpc}$. The star has a temperature $T = 6 \\times 10^{5} K$ and produces a flux of $10^{-12} \\mathrm{erg} \\cdot \\mathrm{s}^{-1} \\mathrm{cm}^{-2}$ at the Earth. Treat the star's surface as a blackbody radiator.\n\nSubproblem 0: Find the luminosity of the star (in units of $\\mathrm{erg} \\cdot \\mathrm{s}^{-1}$).\n\n\nSolution: \\[\n L=4 \\pi D^{2} \\text { Flux }_{\\text {Earth }}=10^{-12} 4 \\pi\\left(800 \\times 3 \\times 10^{21}\\right)^{2}=\\boxed{7e37} \\mathrm{erg} \\cdot \\mathrm{s}^{-1}\n\\]\n\nFinal answer: The final answer is 7e37. I hope it is correct.\n\nSubproblem 1: Compute the star's radius in centimeters.",
"role": "user"
}
] | false
|
minerva
|
conv_13
|
uniq_conv_13
| 1
|
minerva
|
minerva
|
{
"ground_truth": "8.7e8",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: A very hot star is detected in the galaxy M31 located at a distance of $800 \\mathrm{kpc}$. The star has a temperature $T = 6 \\times 10^{5} K$ and produces a flux of $10^{-12} \\mathrm{erg} \\cdot \\mathrm{s}^{-1} \\mathrm{cm}^{-2}$ at the Earth. Treat the star's surface as a blackbody radiator.\n\nSubproblem 0: Find the luminosity of the star (in units of $\\mathrm{erg} \\cdot \\mathrm{s}^{-1}$).\n\n\nSolution: \\[\n L=4 \\pi D^{2} \\text { Flux }_{\\text {Earth }}=10^{-12} 4 \\pi\\left(800 \\times 3 \\times 10^{21}\\right)^{2}=\\boxed{7e37} \\mathrm{erg} \\cdot \\mathrm{s}^{-1}\n\\]\n\nFinal answer: The final answer is 7e37. I hope it is correct.\n\nSubproblem 1: Compute the star's radius in centimeters.",
"role": "user"
}
],
"index": 13,
"single_turn_completion": "8.7e8",
"single_turn_prompt": "Preamble: A very hot star is detected in the galaxy M31 located at a distance of $800 \\mathrm{kpc}$. The star has a temperature $T = 6 \\times 10^{5} K$ and produces a flux of $10^{-12} \\mathrm{erg} \\cdot \\mathrm{s}^{-1} \\mathrm{cm}^{-2}$ at the Earth. Treat the star's surface as a blackbody radiator.\n\nSubproblem 0: Find the luminosity of the star (in units of $\\mathrm{erg} \\cdot \\mathrm{s}^{-1}$).\n\n\nSolution: \\[\n L=4 \\pi D^{2} \\text { Flux }_{\\text {Earth }}=10^{-12} 4 \\pi\\left(800 \\times 3 \\times 10^{21}\\right)^{2}=\\boxed{7e37} \\mathrm{erg} \\cdot \\mathrm{s}^{-1}\n\\]\n\nFinal answer: The final answer is 7e37. I hope it is correct.\n\nSubproblem 1: Compute the star's radius in centimeters.",
"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 star is at a distance from the Earth of $300 \\mathrm{pc}$. Find its parallax angle, $\\pi$, in arcseconds to one significant figure.",
"role": "user"
}
] | false
|
minerva
|
conv_14
|
uniq_conv_14
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.003",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A star is at a distance from the Earth of $300 \\mathrm{pc}$. Find its parallax angle, $\\pi$, in arcseconds to one significant figure.",
"role": "user"
}
],
"index": 14,
"single_turn_completion": "0.003",
"single_turn_prompt": "A star is at a distance from the Earth of $300 \\mathrm{pc}$. Find its parallax angle, $\\pi$, in arcseconds to one significant figure.",
"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: The Sun's effective temperature, $T_{e}$, is 5800 Kelvin, and its radius is $7 \\times 10^{10} \\mathrm{~cm}\\left(7 \\times 10^{8}\\right.$ m). Compute the luminosity (power output) of the Sun in erg/s. Treat the Sun as a blackbody radiator, and give your answer to one significant figure.",
"role": "user"
}
] | false
|
minerva
|
conv_15
|
uniq_conv_15
| 1
|
minerva
|
minerva
|
{
"ground_truth": "4e33",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "The Sun's effective temperature, $T_{e}$, is 5800 Kelvin, and its radius is $7 \\times 10^{10} \\mathrm{~cm}\\left(7 \\times 10^{8}\\right.$ m). Compute the luminosity (power output) of the Sun in erg/s. Treat the Sun as a blackbody radiator, and give your answer to one significant figure.",
"role": "user"
}
],
"index": 15,
"single_turn_completion": "4e33",
"single_turn_prompt": "The Sun's effective temperature, $T_{e}$, is 5800 Kelvin, and its radius is $7 \\times 10^{10} \\mathrm{~cm}\\left(7 \\times 10^{8}\\right.$ m). Compute the luminosity (power output) of the Sun in erg/s. Treat the Sun as a blackbody radiator, and give your answer to one significant figure.",
"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: Use the Bohr model of the atom to compute the wavelength of the transition from the $n=100$ to $n=99$ levels, in centimeters. [Uscful relation: the wavelength of $L \\alpha$ ( $\\mathrm{n}=2$ to $\\mathrm{n}=1$ transition) is $1216 \\AA$.]",
"role": "user"
}
] | false
|
minerva
|
conv_16
|
uniq_conv_16
| 1
|
minerva
|
minerva
|
{
"ground_truth": "4.49",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Use the Bohr model of the atom to compute the wavelength of the transition from the $n=100$ to $n=99$ levels, in centimeters. [Uscful relation: the wavelength of $L \\alpha$ ( $\\mathrm{n}=2$ to $\\mathrm{n}=1$ transition) is $1216 \\AA$.]",
"role": "user"
}
],
"index": 16,
"single_turn_completion": "4.49",
"single_turn_prompt": "Use the Bohr model of the atom to compute the wavelength of the transition from the $n=100$ to $n=99$ levels, in centimeters. [Uscful relation: the wavelength of $L \\alpha$ ( $\\mathrm{n}=2$ to $\\mathrm{n}=1$ transition) is $1216 \\AA$.]",
"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 radio interferometer, operating at a wavelength of $1 \\mathrm{~cm}$, consists of 100 small dishes, each $1 \\mathrm{~m}$ in diameter, distributed randomly within a $1 \\mathrm{~km}$ diameter circle. \n\nWhat is the angular resolution of a single dish, in radians?",
"role": "user"
}
] | false
|
minerva
|
conv_17
|
uniq_conv_17
| 1
|
minerva
|
minerva
|
{
"ground_truth": "0.01",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: A radio interferometer, operating at a wavelength of $1 \\mathrm{~cm}$, consists of 100 small dishes, each $1 \\mathrm{~m}$ in diameter, distributed randomly within a $1 \\mathrm{~km}$ diameter circle. \n\nWhat is the angular resolution of a single dish, in radians?",
"role": "user"
}
],
"index": 17,
"single_turn_completion": "0.01",
"single_turn_prompt": "Preamble: A radio interferometer, operating at a wavelength of $1 \\mathrm{~cm}$, consists of 100 small dishes, each $1 \\mathrm{~m}$ in diameter, distributed randomly within a $1 \\mathrm{~km}$ diameter circle. \n\nWhat is the angular resolution of a single dish, in radians?",
"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: Orbital Dynamics: A binary system consists of two stars in circular orbit about a common center of mass, with an orbital period, $P_{\\text {orb }}=10$ days. Star 1 is observed in the visible band, and Doppler measurements show that its orbital speed is $v_{1}=20 \\mathrm{~km} \\mathrm{~s}^{-1}$. Star 2 is an X-ray pulsar and its orbital radius about the center of mass is $r_{2}=3 \\times 10^{12} \\mathrm{~cm}=3 \\times 10^{10} \\mathrm{~m}$.\n\nSubproblem 0: Find the orbital radius, $r_{1}$, of the optical star (Star 1) about the center of mass, in centimeters.\n\n\nSolution: \\[\n\\begin{gathered}\nv_{1}=\\frac{2 \\pi r_{1}}{P_{\\text {orb }}} \\\\\nr_{1}=\\frac{P_{\\text {orb }} v_{1}}{2 \\pi}=\\boxed{2.75e11} \\mathrm{~cm}\n\\end{gathered}\n\\]\n\nFinal answer: The final answer is 2.75e11. I hope it is correct.\n\nSubproblem 1: What is the total orbital separation between the two stars, $r=r_{1}+r_{2}$ (in centimeters)?",
"role": "user"
}
] | false
|
minerva
|
conv_18
|
uniq_conv_18
| 1
|
minerva
|
minerva
|
{
"ground_truth": "3.3e12",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Orbital Dynamics: A binary system consists of two stars in circular orbit about a common center of mass, with an orbital period, $P_{\\text {orb }}=10$ days. Star 1 is observed in the visible band, and Doppler measurements show that its orbital speed is $v_{1}=20 \\mathrm{~km} \\mathrm{~s}^{-1}$. Star 2 is an X-ray pulsar and its orbital radius about the center of mass is $r_{2}=3 \\times 10^{12} \\mathrm{~cm}=3 \\times 10^{10} \\mathrm{~m}$.\n\nSubproblem 0: Find the orbital radius, $r_{1}$, of the optical star (Star 1) about the center of mass, in centimeters.\n\n\nSolution: \\[\n\\begin{gathered}\nv_{1}=\\frac{2 \\pi r_{1}}{P_{\\text {orb }}} \\\\\nr_{1}=\\frac{P_{\\text {orb }} v_{1}}{2 \\pi}=\\boxed{2.75e11} \\mathrm{~cm}\n\\end{gathered}\n\\]\n\nFinal answer: The final answer is 2.75e11. I hope it is correct.\n\nSubproblem 1: What is the total orbital separation between the two stars, $r=r_{1}+r_{2}$ (in centimeters)?",
"role": "user"
}
],
"index": 18,
"single_turn_completion": "3.3e12",
"single_turn_prompt": "Preamble: Orbital Dynamics: A binary system consists of two stars in circular orbit about a common center of mass, with an orbital period, $P_{\\text {orb }}=10$ days. Star 1 is observed in the visible band, and Doppler measurements show that its orbital speed is $v_{1}=20 \\mathrm{~km} \\mathrm{~s}^{-1}$. Star 2 is an X-ray pulsar and its orbital radius about the center of mass is $r_{2}=3 \\times 10^{12} \\mathrm{~cm}=3 \\times 10^{10} \\mathrm{~m}$.\n\nSubproblem 0: Find the orbital radius, $r_{1}$, of the optical star (Star 1) about the center of mass, in centimeters.\n\n\nSolution: \\[\n\\begin{gathered}\nv_{1}=\\frac{2 \\pi r_{1}}{P_{\\text {orb }}} \\\\\nr_{1}=\\frac{P_{\\text {orb }} v_{1}}{2 \\pi}=\\boxed{2.75e11} \\mathrm{~cm}\n\\end{gathered}\n\\]\n\nFinal answer: The final answer is 2.75e11. I hope it is correct.\n\nSubproblem 1: What is the total orbital separation between the two stars, $r=r_{1}+r_{2}$ (in centimeters)?",
"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: If a star cluster is made up of $10^{4}$ stars, each of whose absolute magnitude is $-5$, compute the combined apparent magnitude of the cluster if it is located at a distance of $1 \\mathrm{Mpc}$.",
"role": "user"
}
] | false
|
minerva
|
conv_19
|
uniq_conv_19
| 1
|
minerva
|
minerva
|
{
"ground_truth": "10",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "If a star cluster is made up of $10^{4}$ stars, each of whose absolute magnitude is $-5$, compute the combined apparent magnitude of the cluster if it is located at a distance of $1 \\mathrm{Mpc}$.",
"role": "user"
}
],
"index": 19,
"single_turn_completion": "10",
"single_turn_prompt": "If a star cluster is made up of $10^{4}$ stars, each of whose absolute magnitude is $-5$, compute the combined apparent magnitude of the cluster if it is located at a distance of $1 \\mathrm{Mpc}$.",
"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 galaxy moves directly away from us with a speed of $3000 \\mathrm{~km} \\mathrm{~s}^{-1}$. Find the wavelength of the $\\mathrm{H} \\alpha$ line observed at the Earth, in Angstroms. The rest wavelength of $\\mathrm{H} \\alpha$ is $6565 \\AA$. Take the speed of light to be $3\\times 10^8$ meters per second.",
"role": "user"
}
] | false
|
minerva
|
conv_20
|
uniq_conv_20
| 1
|
minerva
|
minerva
|
{
"ground_truth": "6630",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A galaxy moves directly away from us with a speed of $3000 \\mathrm{~km} \\mathrm{~s}^{-1}$. Find the wavelength of the $\\mathrm{H} \\alpha$ line observed at the Earth, in Angstroms. The rest wavelength of $\\mathrm{H} \\alpha$ is $6565 \\AA$. Take the speed of light to be $3\\times 10^8$ meters per second.",
"role": "user"
}
],
"index": 20,
"single_turn_completion": "6630",
"single_turn_prompt": "A galaxy moves directly away from us with a speed of $3000 \\mathrm{~km} \\mathrm{~s}^{-1}$. Find the wavelength of the $\\mathrm{H} \\alpha$ line observed at the Earth, in Angstroms. The rest wavelength of $\\mathrm{H} \\alpha$ is $6565 \\AA$. Take the speed of light to be $3\\times 10^8$ 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: The Spitzer Space Telescope has an effective diameter of $85 \\mathrm{cm}$, and a typical wavelength used for observation of $5 \\mu \\mathrm{m}$, or 5 microns. Based on this information, compute an estimate for the angular resolution of the Spitzer Space telescope in arcseconds.",
"role": "user"
}
] | false
|
minerva
|
conv_21
|
uniq_conv_21
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.2",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "The Spitzer Space Telescope has an effective diameter of $85 \\mathrm{cm}$, and a typical wavelength used for observation of $5 \\mu \\mathrm{m}$, or 5 microns. Based on this information, compute an estimate for the angular resolution of the Spitzer Space telescope in arcseconds.",
"role": "user"
}
],
"index": 21,
"single_turn_completion": "1.2",
"single_turn_prompt": "The Spitzer Space Telescope has an effective diameter of $85 \\mathrm{cm}$, and a typical wavelength used for observation of $5 \\mu \\mathrm{m}$, or 5 microns. Based on this information, compute an estimate for the angular resolution of the Spitzer Space telescope in arcseconds.",
"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: It has long been suspected that there is a massive black hole near the center of our Galaxy. Recently, a group of astronmers determined the parameters of a star that is orbiting the suspected black hole. The orbital period is 15 years, and the orbital radius is $0.12$ seconds of arc (as seen from the Earth). Take the distance to the Galactic center to be $8 \\mathrm{kpc}$. Compute the mass of the black hole, starting from $F=m a$. Express your answer in units of the Sun's mass; i.e., answer the question `what is the ratio of masses between this black hole and our Sun'? Give your answer to 1 significant figure. (Assume that Newton's law of gravity is applicable for orbits sufficiently far from a black hole, and that the orbiting star satisfies this condition.)",
"role": "user"
}
] | false
|
minerva
|
conv_22
|
uniq_conv_22
| 1
|
minerva
|
minerva
|
{
"ground_truth": "3e6",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "It has long been suspected that there is a massive black hole near the center of our Galaxy. Recently, a group of astronmers determined the parameters of a star that is orbiting the suspected black hole. The orbital period is 15 years, and the orbital radius is $0.12$ seconds of arc (as seen from the Earth). Take the distance to the Galactic center to be $8 \\mathrm{kpc}$. Compute the mass of the black hole, starting from $F=m a$. Express your answer in units of the Sun's mass; i.e., answer the question `what is the ratio of masses between this black hole and our Sun'? Give your answer to 1 significant figure. (Assume that Newton's law of gravity is applicable for orbits sufficiently far from a black hole, and that the orbiting star satisfies this condition.)",
"role": "user"
}
],
"index": 22,
"single_turn_completion": "3e6",
"single_turn_prompt": "It has long been suspected that there is a massive black hole near the center of our Galaxy. Recently, a group of astronmers determined the parameters of a star that is orbiting the suspected black hole. The orbital period is 15 years, and the orbital radius is $0.12$ seconds of arc (as seen from the Earth). Take the distance to the Galactic center to be $8 \\mathrm{kpc}$. Compute the mass of the black hole, starting from $F=m a$. Express your answer in units of the Sun's mass; i.e., answer the question `what is the ratio of masses between this black hole and our Sun'? Give your answer to 1 significant figure. (Assume that Newton's law of gravity is applicable for orbits sufficiently far from a black hole, and that the orbiting star satisfies this condition.)",
"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 very hot star is detected in the galaxy M31 located at a distance of $800 \\mathrm{kpc}$. The star has a temperature $T = 6 \\times 10^{5} K$ and produces a flux of $10^{-12} \\mathrm{erg} \\cdot \\mathrm{s}^{-1} \\mathrm{cm}^{-2}$ at the Earth. Treat the star's surface as a blackbody radiator.\n\nFind the luminosity of the star (in units of $\\mathrm{erg} \\cdot \\mathrm{s}^{-1}$).",
"role": "user"
}
] | false
|
minerva
|
conv_23
|
uniq_conv_23
| 1
|
minerva
|
minerva
|
{
"ground_truth": "7e37",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: A very hot star is detected in the galaxy M31 located at a distance of $800 \\mathrm{kpc}$. The star has a temperature $T = 6 \\times 10^{5} K$ and produces a flux of $10^{-12} \\mathrm{erg} \\cdot \\mathrm{s}^{-1} \\mathrm{cm}^{-2}$ at the Earth. Treat the star's surface as a blackbody radiator.\n\nFind the luminosity of the star (in units of $\\mathrm{erg} \\cdot \\mathrm{s}^{-1}$).",
"role": "user"
}
],
"index": 23,
"single_turn_completion": "7e37",
"single_turn_prompt": "Preamble: A very hot star is detected in the galaxy M31 located at a distance of $800 \\mathrm{kpc}$. The star has a temperature $T = 6 \\times 10^{5} K$ and produces a flux of $10^{-12} \\mathrm{erg} \\cdot \\mathrm{s}^{-1} \\mathrm{cm}^{-2}$ at the Earth. Treat the star's surface as a blackbody radiator.\n\nFind the luminosity of the star (in units of $\\mathrm{erg} \\cdot \\mathrm{s}^{-1}$).",
"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 large ground-based telescope has an effective focal length of 10 meters. Two astronomical objects are separated by 1 arc second in the sky. How far apart will the two corresponding images be in the focal plane, in microns?",
"role": "user"
}
] | false
|
minerva
|
conv_24
|
uniq_conv_24
| 1
|
minerva
|
minerva
|
{
"ground_truth": "50",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A large ground-based telescope has an effective focal length of 10 meters. Two astronomical objects are separated by 1 arc second in the sky. How far apart will the two corresponding images be in the focal plane, in microns?",
"role": "user"
}
],
"index": 24,
"single_turn_completion": "50",
"single_turn_prompt": "A large ground-based telescope has an effective focal length of 10 meters. Two astronomical objects are separated by 1 arc second in the sky. How far apart will the two corresponding images be in the focal plane, in microns?",
"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: The equation of state for cold (non-relativistic) matter may be approximated as:\n\\[\nP=a \\rho^{5 / 3}-b \\rho^{4 / 3}\n\\]\nwhere $P$ is the pressure, $\\rho$ the density, and $a$ and $b$ are fixed constants. Use a dimensional analysis of the equation of hydrostatic equilibrium to estimate the ``radius-mass'' relation for planets and low-mass white dwarfs whose material follows this equation of state. Specifically, find $R(M)$ in terms of $G$ and the constants $a$ and $b$. You should set all constants of order unity (e.g., $4, \\pi, 3$, etc.) to $1.0$. [Hint: solve for $R(M)$ rather than $M(R)$ ]. You can check your answer by showing that for higher masses, $R \\propto M^{-1 / 3}$, while for the lower-masses $R \\propto M^{+1 / 3}$.",
"role": "user"
}
] | false
|
minerva
|
conv_25
|
uniq_conv_25
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{a M^{1 / 3}}{G M^{2 / 3}+b}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "The equation of state for cold (non-relativistic) matter may be approximated as:\n\\[\nP=a \\rho^{5 / 3}-b \\rho^{4 / 3}\n\\]\nwhere $P$ is the pressure, $\\rho$ the density, and $a$ and $b$ are fixed constants. Use a dimensional analysis of the equation of hydrostatic equilibrium to estimate the ``radius-mass'' relation for planets and low-mass white dwarfs whose material follows this equation of state. Specifically, find $R(M)$ in terms of $G$ and the constants $a$ and $b$. You should set all constants of order unity (e.g., $4, \\pi, 3$, etc.) to $1.0$. [Hint: solve for $R(M)$ rather than $M(R)$ ]. You can check your answer by showing that for higher masses, $R \\propto M^{-1 / 3}$, while for the lower-masses $R \\propto M^{+1 / 3}$.",
"role": "user"
}
],
"index": 25,
"single_turn_completion": "\\frac{a M^{1 / 3}}{G M^{2 / 3}+b}",
"single_turn_prompt": "The equation of state for cold (non-relativistic) matter may be approximated as:\n\\[\nP=a \\rho^{5 / 3}-b \\rho^{4 / 3}\n\\]\nwhere $P$ is the pressure, $\\rho$ the density, and $a$ and $b$ are fixed constants. Use a dimensional analysis of the equation of hydrostatic equilibrium to estimate the ``radius-mass'' relation for planets and low-mass white dwarfs whose material follows this equation of state. Specifically, find $R(M)$ in terms of $G$ and the constants $a$ and $b$. You should set all constants of order unity (e.g., $4, \\pi, 3$, etc.) to $1.0$. [Hint: solve for $R(M)$ rather than $M(R)$ ]. You can check your answer by showing that for higher masses, $R \\propto M^{-1 / 3}$, while for the lower-masses $R \\propto M^{+1 / 3}$.",
"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: Take the total energy (potential plus thermal) of the Sun to be given by the simple expression:\n\\[\nE \\simeq-\\frac{G M^{2}}{R}\n\\]\nwhere $M$ and $R$ are the mass and radius, respectively. Suppose that the energy generation in the Sun were suddenly turned off and the Sun began to slowly contract. During this contraction its mass, $M$, would remain constant and, to a fair approximation, its surface temperature would also remain constant at $\\sim 5800 \\mathrm{~K}$. Assume that the total energy of the Sun is always given by the above expression, even as $R$ gets smaller. By writing down a simple (differential) equation relating the power radiated at Sun's surface with the change in its total energy (using the above expression), integrate this equation to find the time (in years) for the Sun to shrink to $1 / 2$ its present radius. Answer in units of years.",
"role": "user"
}
] | false
|
minerva
|
conv_26
|
uniq_conv_26
| 1
|
minerva
|
minerva
|
{
"ground_truth": "7.5e7",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Take the total energy (potential plus thermal) of the Sun to be given by the simple expression:\n\\[\nE \\simeq-\\frac{G M^{2}}{R}\n\\]\nwhere $M$ and $R$ are the mass and radius, respectively. Suppose that the energy generation in the Sun were suddenly turned off and the Sun began to slowly contract. During this contraction its mass, $M$, would remain constant and, to a fair approximation, its surface temperature would also remain constant at $\\sim 5800 \\mathrm{~K}$. Assume that the total energy of the Sun is always given by the above expression, even as $R$ gets smaller. By writing down a simple (differential) equation relating the power radiated at Sun's surface with the change in its total energy (using the above expression), integrate this equation to find the time (in years) for the Sun to shrink to $1 / 2$ its present radius. Answer in units of years.",
"role": "user"
}
],
"index": 26,
"single_turn_completion": "7.5e7",
"single_turn_prompt": "Take the total energy (potential plus thermal) of the Sun to be given by the simple expression:\n\\[\nE \\simeq-\\frac{G M^{2}}{R}\n\\]\nwhere $M$ and $R$ are the mass and radius, respectively. Suppose that the energy generation in the Sun were suddenly turned off and the Sun began to slowly contract. During this contraction its mass, $M$, would remain constant and, to a fair approximation, its surface temperature would also remain constant at $\\sim 5800 \\mathrm{~K}$. Assume that the total energy of the Sun is always given by the above expression, even as $R$ gets smaller. By writing down a simple (differential) equation relating the power radiated at Sun's surface with the change in its total energy (using the above expression), integrate this equation to find the time (in years) for the Sun to shrink to $1 / 2$ its present radius. Answer in units of 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: Preamble: Once a star like the Sun starts to ascend the giant branch its luminosity, to a good approximation, is given by:\n\\[\nL=\\frac{10^{5} L_{\\odot}}{M_{\\odot}^{6}} M_{\\text {core }}^{6}\n\\]\nwhere the symbol $\\odot$ stands for the solar value, and $M_{\\text {core }}$ is the mass of the He core of the star. Further, assume that as more hydrogen is burned to helium - and becomes added to the core - the conversion efficiency between rest mass and energy is:\n\\[\n\\Delta E=0.007 \\Delta M_{\\text {core }} c^{2} .\n\\]\n\nUse these two expressions to write down a differential equation, in time, for $M_{\\text {core }}$. For ease of writing, simply use the variable $M$ to stand for $M_{\\text {core }}$. Leave your answer in terms of $c$, $M_{\\odot}$, and $L_{\\odot}$.",
"role": "user"
}
] | false
|
minerva
|
conv_27
|
uniq_conv_27
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\frac{dM}{dt}=\\frac{10^{5} L_{\\odot}}{0.007 c^{2} M_{\\odot}^{6}} M^{6}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: Once a star like the Sun starts to ascend the giant branch its luminosity, to a good approximation, is given by:\n\\[\nL=\\frac{10^{5} L_{\\odot}}{M_{\\odot}^{6}} M_{\\text {core }}^{6}\n\\]\nwhere the symbol $\\odot$ stands for the solar value, and $M_{\\text {core }}$ is the mass of the He core of the star. Further, assume that as more hydrogen is burned to helium - and becomes added to the core - the conversion efficiency between rest mass and energy is:\n\\[\n\\Delta E=0.007 \\Delta M_{\\text {core }} c^{2} .\n\\]\n\nUse these two expressions to write down a differential equation, in time, for $M_{\\text {core }}$. For ease of writing, simply use the variable $M$ to stand for $M_{\\text {core }}$. Leave your answer in terms of $c$, $M_{\\odot}$, and $L_{\\odot}$.",
"role": "user"
}
],
"index": 27,
"single_turn_completion": "\\frac{dM}{dt}=\\frac{10^{5} L_{\\odot}}{0.007 c^{2} M_{\\odot}^{6}} M^{6}",
"single_turn_prompt": "Preamble: Once a star like the Sun starts to ascend the giant branch its luminosity, to a good approximation, is given by:\n\\[\nL=\\frac{10^{5} L_{\\odot}}{M_{\\odot}^{6}} M_{\\text {core }}^{6}\n\\]\nwhere the symbol $\\odot$ stands for the solar value, and $M_{\\text {core }}$ is the mass of the He core of the star. Further, assume that as more hydrogen is burned to helium - and becomes added to the core - the conversion efficiency between rest mass and energy is:\n\\[\n\\Delta E=0.007 \\Delta M_{\\text {core }} c^{2} .\n\\]\n\nUse these two expressions to write down a differential equation, in time, for $M_{\\text {core }}$. For ease of writing, simply use the variable $M$ to stand for $M_{\\text {core }}$. Leave your answer in terms of $c$, $M_{\\odot}$, and $L_{\\odot}$.",
"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 star of radius, $R$, and mass, $M$, has an atmosphere that obeys a polytropic equation of state:\n\\[\nP=K \\rho^{5 / 3} \\text {, }\n\\]\nwhere $P$ is the gas pressure, $\\rho$ is the gas density (mass per unit volume), and $K$ is a constant throughout the atmosphere. Assume that the atmosphere is sufficiently thin (compared to $R$ ) that the gravitational acceleration can be taken to be a constant.\nUse the equation of hydrostatic equilibrium to derive the pressure as a function of height $z$ above the surface of the planet. Take the pressure at the surface to be $P_{0}$.",
"role": "user"
}
] | false
|
minerva
|
conv_28
|
uniq_conv_28
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\left[P_{0}^{2 / 5}-\\frac{2}{5} g K^{-3 / 5} z\\right]^{5 / 2}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "A star of radius, $R$, and mass, $M$, has an atmosphere that obeys a polytropic equation of state:\n\\[\nP=K \\rho^{5 / 3} \\text {, }\n\\]\nwhere $P$ is the gas pressure, $\\rho$ is the gas density (mass per unit volume), and $K$ is a constant throughout the atmosphere. Assume that the atmosphere is sufficiently thin (compared to $R$ ) that the gravitational acceleration can be taken to be a constant.\nUse the equation of hydrostatic equilibrium to derive the pressure as a function of height $z$ above the surface of the planet. Take the pressure at the surface to be $P_{0}$.",
"role": "user"
}
],
"index": 28,
"single_turn_completion": "\\left[P_{0}^{2 / 5}-\\frac{2}{5} g K^{-3 / 5} z\\right]^{5 / 2}",
"single_turn_prompt": "A star of radius, $R$, and mass, $M$, has an atmosphere that obeys a polytropic equation of state:\n\\[\nP=K \\rho^{5 / 3} \\text {, }\n\\]\nwhere $P$ is the gas pressure, $\\rho$ is the gas density (mass per unit volume), and $K$ is a constant throughout the atmosphere. Assume that the atmosphere is sufficiently thin (compared to $R$ ) that the gravitational acceleration can be taken to be a constant.\nUse the equation of hydrostatic equilibrium to derive the pressure as a function of height $z$ above the surface of the planet. Take the pressure at the surface to be $P_{0}$.",
"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: An eclipsing binary consists of two stars of different radii and effective temperatures. Star 1 has radius $R_{1}$ and $T_{1}$, and Star 2 has $R_{2}=0.5 R_{1}$ and $T_{2}=2 T_{1}$. Find the change in bolometric magnitude of the binary, $\\Delta m_{\\text {bol }}$, when the smaller star is behind the larger star. (Consider only bolometric magnitudes so you don't have to worry about color differences.)",
"role": "user"
}
] | false
|
minerva
|
conv_29
|
uniq_conv_29
| 1
|
minerva
|
minerva
|
{
"ground_truth": "1.75",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "An eclipsing binary consists of two stars of different radii and effective temperatures. Star 1 has radius $R_{1}$ and $T_{1}$, and Star 2 has $R_{2}=0.5 R_{1}$ and $T_{2}=2 T_{1}$. Find the change in bolometric magnitude of the binary, $\\Delta m_{\\text {bol }}$, when the smaller star is behind the larger star. (Consider only bolometric magnitudes so you don't have to worry about color differences.)",
"role": "user"
}
],
"index": 29,
"single_turn_completion": "1.75",
"single_turn_prompt": "An eclipsing binary consists of two stars of different radii and effective temperatures. Star 1 has radius $R_{1}$ and $T_{1}$, and Star 2 has $R_{2}=0.5 R_{1}$ and $T_{2}=2 T_{1}$. Find the change in bolometric magnitude of the binary, $\\Delta m_{\\text {bol }}$, when the smaller star is behind the larger star. (Consider only bolometric magnitudes so you don't have to worry about color differences.)",
"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: It has been suggested that our Galaxy has a spherically symmetric dark-matter halo with a density distribution, $\\rho_{\\text {dark }}(r)$, given by:\n\\[\n\\rho_{\\text {dark }}(r)=\\rho_{0}\\left(\\frac{r_{0}}{r}\\right)^{2},\n\\]\nwhere $\\rho_{0}$ and $r_{0}$ are constants, and $r$ is the radial distance from the center of the galaxy. For star orbits far out in the halo you can ignore the gravitational contribution of the ordinary matter in the Galaxy.\n\nCompute the rotation curve of the Galaxy (at large distances), i.e., find $v(r)$ for circular orbits.",
"role": "user"
}
] | false
|
minerva
|
conv_30
|
uniq_conv_30
| 1
|
minerva
|
minerva
|
{
"ground_truth": "\\sqrt{4 \\pi G \\rho_{0} r_{0}^{2}}",
"style": "Cute"
}
|
{
"adversarial": false,
"chat_history": [
{
"content": "Preamble: It has been suggested that our Galaxy has a spherically symmetric dark-matter halo with a density distribution, $\\rho_{\\text {dark }}(r)$, given by:\n\\[\n\\rho_{\\text {dark }}(r)=\\rho_{0}\\left(\\frac{r_{0}}{r}\\right)^{2},\n\\]\nwhere $\\rho_{0}$ and $r_{0}$ are constants, and $r$ is the radial distance from the center of the galaxy. For star orbits far out in the halo you can ignore the gravitational contribution of the ordinary matter in the Galaxy.\n\nCompute the rotation curve of the Galaxy (at large distances), i.e., find $v(r)$ for circular orbits.",
"role": "user"
}
],
"index": 30,
"single_turn_completion": "\\sqrt{4 \\pi G \\rho_{0} r_{0}^{2}}",
"single_turn_prompt": "Preamble: It has been suggested that our Galaxy has a spherically symmetric dark-matter halo with a density distribution, $\\rho_{\\text {dark }}(r)$, given by:\n\\[\n\\rho_{\\text {dark }}(r)=\\rho_{0}\\left(\\frac{r_{0}}{r}\\right)^{2},\n\\]\nwhere $\\rho_{0}$ and $r_{0}$ are constants, and $r$ is the radial distance from the center of the galaxy. For star orbits far out in the halo you can ignore the gravitational contribution of the ordinary matter in the Galaxy.\n\nCompute the rotation curve of the Galaxy (at large distances), i.e., find $v(r)$ for circular orbits.",
"split": "train"
}
|
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