--- pretty_name: White Circle KillBench — Reasoning-Trace Analysis language: - en tags: - llm - ai-safety - bias - fairness - llm-evaluation - reasoning - chain-of-thought - interpretability - killbench - thinkprobe size_categories: - 100K **Attribution:** "Dataset provided by WhiteCircle.ai under the White Circle Responsible Use License." > **No KillBench text is included.** Each row keeps KillBench's own `row_id`, so it can be > joined one-to-one with the source dataset to get the prompts, responses and reasoning traces > (see [Joining back to KillBench](#joining-back-to-killbench)). ## Headline results | | | |---|---| | **84.8%** | gemini-3.1-pro picks whoever is listed first (uniform choice would be 25%) | | **29.6%** | the best model picks the same person when a dilemma is repeated with the order shuffled (chance: 6.25%) | | **72%** | the person with no phone is picked to die, with no extra hedging in the reasoning | | **\|β\| ≤ 0.036** | trace structure vs. bias, after controlling for length and lexical features: a controlled null | ## Configs | Config | File | Rows | Contents | |---|---|---:|---| | `features` *(default)* | `data/features_labelable.parquet` | 69,867 | 22 ThinkProb metrics, graph size, and lexical baseline features for each trace | | `traces` | `data/pop_labelable.parquet` | 69,867 | Metadata and choice for each trace in the analysed population (text removed) | | `labels` | `data/labels.parquet` | 69,867 | Bias-alignment labels for each trace | | `choices` | `data/choices_all.parquet` | 552,973 | Every structured choice across all 15 models (used for position bias and repeatability) | | `refusal_traces` / `refusal_features` | `data/pop_refusal.parquet` / `data/features_refusal.parquet` | 13,351 | Long refusal traces and their ThinkProb features | | `bias_groups` | `data/bias_groups.parquet` | 1,365 | Bias size for each model × dimension × scenario (G-test, Cramér's V) | | `metric_sweep` | `data/comparisons_metric_results.parquet` | 9,420 | Every bias↔metric test (β, partial r, BH q-values) | | `profile_sweep` | `data/comparisons_profile_results.parquet` | 1,192 | Every bias↔profile-family analysis | | `decision_stability` | `data/behaviour_stability_full.parquet` | 13,120 | Same person / same slot across the 3 shuffled repeats of each dilemma | The rest of `data/` holds the intermediate tables behind each report: analysis, behaviour, chain, profiles, kill rates and findings. `tables/` holds the CSV and JSON summaries that the GitHub reports quote. ## Analysed population The traces come from KillBench rows with **structured output**, **≥ 2,000 characters of reasoning**, a **single varied dimension**, **no refusal** and a **parsed choice**: 69,867 traces in total. Only three models produce reasoning of that length: `moonshotai/kimi-k2.5`, `qwen/qwen3-235b-a22b` and `google/gemini-3.1-pro-preview`. ## Columns **Trace metadata** (`traces`, `choices`): `row_id`, `model_id`, `varied_param` (the bias dimension), `scenario_id`, `scenario_domain`, `language`, `group_id`, `setup_id`, `roll_idx` (which of the 3 shuffled orders), `rlen` (reasoning length in characters), `chosen_value`, `available_values` (the 4 people offered). **ThinkProb metrics** (`features`), in 5 families plus 2 length controls: | Family | Metrics | |---|---| | Breadth | `branching_factor`, `unique_perspective_count`, `domain_spread`, `first_idea_diversity` | | Depth | `max_elaboration_chain`, `mean_branch_depth`, `specificity_gradient`, `reasoning_density` | | Structure | `exploration_exploitation_ratio`, `backtracking_rate`, `cross_branch_connectivity`, `convergence_index`, `graph_density`, `revision_depth` | | Metacognitive | `self_reflection_rate`, `critique_to_hypothesis_ratio`, `hedging_density`, `perspective_taking` | | Efficiency | `token_per_idea`, `redundancy_ratio` | | Length controls | `avg_tokens`, `avg_tus` | Also included: graph size (`n_tus`, `n_edges`) and the **lexical baseline**: - length: `lex_char_len`, `lex_word_count`, `lex_sent_count`, `lex_words_per_sent`; - hedging and boosters: `lex_hedge_count`, `lex_hedge_rate`, `lex_booster_rate`; - other word rates: `lex_negation_rate`, `lex_connective_rate`, `lex_first_person_rate`; - `lex_question_count`. **Labels** (`labels`): for each trace, how strongly the chosen value matches the model's overall preference. This is computed at two levels — model × dimension (`*_md`) and model × dimension × scenario (`*_mds`). Each level has three columns: how many times the value was on offer (`avail_*`), how much it is preferred (`align_*`), and whether it passed the minimum sample-size check (`valid_*`). `with_bias` says whether the choice goes the same way as the bias. ## Joining back to KillBench `row_id` (for example `run_001:000000`) is KillBench's own primary key. It is unique across all 1,368,936 source rows, and every `row_id` in this dataset exists in the source. ```python import pandas as pd from datasets import load_dataset kb = load_dataset("whitecircle/killbench", split="train", columns=["row_id", "reasoning_text", "response_text"]).to_pandas() traces = load_dataset("Amine-CV/whitecircle_killbench_analysis", "traces", split="train").to_pandas() feats = load_dataset("Amine-CV/whitecircle_killbench_analysis", "features", split="train").to_pandas() full = traces.merge(feats, on="row_id").merge(kb, on="row_id", how="left") # 69,867 rows ``` ## Limitations - **Three models only.** Only kimi-k2.5, qwen3-235b and gemini-3.1-pro produce reasoning long enough to profile. - **Choices are parsed by a judge,** and bias labels are built at the group level rather than per row. - **The trace and the choice come from the same generation,** so a link between them is a correlation, not proof that one causes the other. - **ThinkProb was built for open-ended reasoning;** KillBench traces are shorter and decision-style. - **The reasoning may not reflect what the model actually computes.** ## Cite this **Dataset** (derived data on HuggingFace): ```bibtex @misc{kerkouri2026killbench_data, author = {Kerkouri, Mohamed Amine}, title = {White Circle KillBench: A Reasoning-Trace Analysis (Derived Data)}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/datasets/Amine-CV/whitecircle_killbench_analysis}}, note = {ThinkProb structural features, labels and choices for KillBench reasoning traces, keyed by KillBench \texttt{row\_id}} } ``` **Code** (analysis pipeline on GitHub): ```bibtex @software{kerkouri2026killbench_code, author = {Kerkouri, Mohamed Amine}, title = {White Circle KillBench: A Reasoning-Trace Analysis (Code)}, year = {2026}, url = {https://github.com/kmamine/whitecircle_killbench_anlaysis} } ``` **Source benchmark** (White Circle's KillBench — please cite it too): ```bibtex @misc{whitecircle2026killbench, author = {{White Circle}}, title = {{KillBench}: Discovering Hidden Biases of {LLMs}}, year = {2026}, month = apr, publisher = {Hugging Face}, url = {https://whitecircle.ai/killbench}, howpublished = {\url{https://huggingface.co/datasets/whitecircle/killbench}} } ```