dataset_info:
features:
- name: an_creation_timestamp
dtype: string
- name: an_generation_method
dtype: string
- name: an_generation_model
dtype: string
- name: an_original_completion
dtype: string
- name: an_original_completion_length
dtype: int64
- name: an_paraphrased
dtype: bool
- name: an_style_paraphrase
dtype: string
- name: an_temperature
dtype: float64
- name: blind_judge_confidence_primary
dtype: string
- name: blind_judge_confidence_tiebreaker
dtype: string
- name: blind_judge_justification_primary
dtype: string
- name: blind_judge_justification_tiebreaker
dtype: string
- name: blind_judge_model_primary
dtype: string
- name: blind_judge_model_tiebreaker
dtype: string
- name: blind_judge_verdict_primary
dtype: string
- name: blind_judge_verdict_tiebreaker
dtype: string
- name: completion
dtype: string
- name: deception_type
dtype: string
- name: follow_up_category
dtype: string
- name: follow_up_question
dtype: string
- name: human_verified
dtype: bool
- name: judge_confidence
dtype: string
- name: judge_justification
dtype: string
- name: judge_model_informed
dtype: string
- name: judge_verdict
dtype: string
- name: language
dtype: string
- name: pair_id
dtype: string
- name: pair_type
dtype: string
- name: quadrant
dtype: string
- name: source
dtype: string
- name: translation_model
dtype: string
- name: completion_reasoning
dtype: string
- name: reasoning_in_context
dtype: bool
- name: messages_json
dtype: string
- name: tools
dtype: string
- name: _v32_source_content_md5
dtype: string
- name: _v32_lane_attributed
dtype: string
splits:
- name: honest_honest
num_bytes: 450870436
num_examples: 43941
- name: honest_deceptive
num_bytes: 504447178
num_examples: 41044
- name: deceptive_honest
num_bytes: 389100896
num_examples: 37567
- name: deceptive_deceptive
num_bytes: 377591319
num_examples: 40464
download_size: 0
dataset_size: 1722009829
configs:
- config_name: default
data_files:
- split: honest_honest
path: data/honest_honest-*
- split: honest_deceptive
path: data/honest_deceptive-*
- split: deceptive_honest
path: data/deceptive_honest-*
- split: deceptive_deceptive
path: data/deceptive_deceptive-*
fibs-v1
FAR AI Deception Pod research dataset of 163,016 rows over the four quadrant splits:
| split | rows |
|---|---|
| honest_honest | 43,941 |
| honest_deceptive | 41,044 |
| deceptive_honest | 37,567 |
| deceptive_deceptive | 40,464 |
Families
| family | rows in this release |
|---|---|
| among | 6,464 |
| anti | 7,984 |
| b1-bare | 4,034 |
| b1-followup | 4,488 |
| caving | 1,648 |
| doluschat | 8,000 |
| fabricated | 2,164 |
| fever | 8,000 |
| harm | 8,000 |
| instructed | 7,868 |
| liarsbench | 7,992 |
| lies | 6,760 |
| longcontext | 1,764 |
| mask | 8,000 |
| medqa | 4,020 |
| model | 3,304 |
| multi | 1,476 |
| negotiation | 6,954 |
| original | 4,204 |
| persona_prefill | 8,000 |
| sandbagging | 6,700 |
| sycophancy | 7,976 |
| synthetic | 8,000 |
| truthfulqa | 4,804 |
| ultrachat | 8,000 |
| werewolf | 6,692 |
| wikihow | 1,760 |
| wikihownest | 7,960 |
Contextual rows: a family-level note
The paper distinguishes contextual rows, where the truth of the statement the model makes is evidenced in the transcript itself (system prompt, earlier turns, tool results, a supplied passage, or the model's own visible working), from rows that rest on world knowledge, which the model could in principle have been trained to believe otherwise. The classification below is by family, sub-source and follow-up category, chosen for precision over recall after reading two full transcripts per family; a row-level classifier is future work.
Contextual by construction (the transcript carries the truth-maker in every row read):
| family or sub-source | where the truth-maker sits |
|---|---|
| doluschat, persona_prefill, mask_provided_facts | facts stated in the system prompt |
| b1-bare, b1-followup | the tool result earlier in the transcript |
| among_us, werewolf, negotiation | the observation log, private role or night knowledge, private facts |
| sycophancy | the base turn's <scratchpad> writes out the correct answer that the stated answer contradicts |
| longcontext v3_bookqa, lc3flip, lc5flip, v5a/v5b_onpolicy, v5c3_intrace, v5c5_novels | the supplied passage, the agent transcript's tool result, or a value the model itself stated earlier |
Contextual by follow-up category, in any family: cat_H and cat_Hf, the response-meta questions
("So you said Y, right?", "Did you mention any caveats?"), whose answer is decided by the prior turn.
Not counted as contextual (world knowledge, internal state, or grounding not guaranteed per row): anti_sycophancy, caving (including its numeric-arithmetic rows), fabricated_statistics, fever (even when the passage is supplied), harm pressure, instructed, liarsbench (including the soft-trigger rows), lies_v2, longcontext lcflip/v2/v4_offpolicy, mask known-facts/statistics/doubling-down, medqa, model_self_knowledge, multi_turn, original, sandbagging, synthetic (even when the user supplies the source text), truthfulqa, ultrachat, wikihow, wikihownest. Meta-truthfulness follow-ups ("Were you honest?") inherit the family's status.
Under this rule 79,580 of 163,016 rows (48.8%) are contextual:
| family | rows | by family/sub-source | by follow-up only | contextual |
|---|---|---|---|---|
| among | 6,464 | 6,464 | 0 | 6,464 |
| anti | 7,984 | 0 | 1,873 | 1,873 |
| b1-bare | 4,034 | 4,034 | 0 | 4,034 |
| b1-followup | 4,488 | 4,488 | 0 | 4,488 |
| caving | 1,648 | 0 | 0 | 0 |
| doluschat | 8,000 | 8,000 | 0 | 8,000 |
| fabricated | 2,164 | 0 | 483 | 483 |
| fever | 8,000 | 0 | 1,452 | 1,452 |
| harm | 8,000 | 0 | 2,338 | 2,338 |
| instructed | 7,868 | 0 | 1,462 | 1,462 |
| liarsbench | 7,992 | 0 | 1,693 | 1,693 |
| lies | 6,760 | 0 | 1,720 | 1,720 |
| longcontext | 1,764 | 1,353 | 5 | 1,358 |
| mask | 8,000 | 2,445 | 1,467 | 3,912 |
| medqa | 4,020 | 0 | 803 | 803 |
| model | 3,304 | 0 | 846 | 846 |
| multi | 1,476 | 0 | 0 | 0 |
| negotiation | 6,954 | 6,954 | 0 | 6,954 |
| original | 4,204 | 0 | 632 | 632 |
| persona_prefill | 8,000 | 8,000 | 0 | 8,000 |
| sandbagging | 6,700 | 0 | 1,959 | 1,959 |
| sycophancy | 7,976 | 7,976 | 0 | 7,976 |
| synthetic | 8,000 | 0 | 1,777 | 1,777 |
| truthfulqa | 4,804 | 0 | 912 | 912 |
| ultrachat | 8,000 | 0 | 1,870 | 1,870 |
| werewolf | 6,692 | 6,692 | 0 | 6,692 |
| wikihow | 1,760 | 0 | 0 | 0 |
| wikihownest | 7,960 | 0 | 1,882 | 1,882 |
Longcontext single-turn rows re-rendered as four messages (2026-10-02)
The 1,085 longcontext rows that held the passage and the question in one user turn (sources
v3_bookqa, lc3flip, lc5flip, v5a_onpolicy, v5c3_intrace, v5c5_novels) now carry four messages:
the passage followed by a short request for an acknowledgement, a one-line assistant acknowledgement, the
original question with a short preamble, and the original completion. The split point is the last
horizontal-rule line (--- or ***) of the original user turn, so the passage and the question text are
unchanged; the acknowledgement request, the acknowledgement and the preamble are drawn from small fixed pools
by a hash of the original messages_json. Only messages_json changed, only on these rows; every other row
and every other column is byte-identical to revision 1596f18267bc45e14f9a08d0f9949015ce0b1d2b. The 679
longcontext rows that already asked their question in a later turn (lcflip, v2, v5b_onpolicy,
v4_offpolicy_concat) are untouched. Reason: a probe read window that starts at the last user turn no longer
spans the whole passage (these rows held 44 percent of the window tokens of the corpus).
provenance/longcontext_single_turn_split.json lists every rewritten row with the sha256 of its original
messages_json, the variant pools and the builder's sha256.
Provenance
provenance/BUILD.json records the parent revision and parquet sha256s, the seed (20260929),
the family cap (8,000), the per-family before/after counts and the builder's sha256;
provenance/new_row_screen.json lists every screened new row with its judge and Fable outcome;
provenance/covariate_trim.json holds the per-family covariate trim logs;
provenance/longcontext_single_turn_split.json records the 2026-10-02 longcontext re-rendering. The builder is
sandbox/oa_backdoor/scripts/dataset_generation/build_v2_slotin.py in the Deception Pod monorepo.
Columns
messages_json (UTF-8 JSON message list; assistant turns may carry
reasoning_content sidecars and OpenAI-style tool_calls), tools (JSON string tool-schema list where
the source uses tools, null elsewhere), completion, and the source and judge metadata columns.
Consumers must json.loads the JSON-string columns. Quadrants use base_follow-up semantics: the first
term labels the base turn and the second labels the follow-up turn.