Amine-CV/whitecircle_killbench_analysis

White Circle KillBench — Reasoning-Trace Analysis

Derived data from an independent analysis of whitecircle/killbench, White Circle's benchmark of demographic bias in life-or-death decisions. Every reasoning trace from that dataset was profiled with ThinkProb, which turns a trace into a thought graph and computes 22 structural metrics without generating any text.

• 📄 Code, reports and figures: github.com/kmamine/whitecircle_killbench_anlaysis
• 🗃️ Source dataset: whitecircle/killbench

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).

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.

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):

@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):

@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):

@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}}
}