Datasets:

Amine-CV
/

whitecircle_killbench_analysis

---
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<n<1M
task_categories:
- text-classification
configs:
- config_name: features
  data_files: data/features_labelable.parquet
  default: true
- config_name: traces
  data_files: data/pop_labelable.parquet
- config_name: labels
  data_files: data/labels.parquet
- config_name: choices
  data_files: data/choices_all.parquet
- config_name: refusal_traces
  data_files: data/pop_refusal.parquet
- config_name: refusal_features
  data_files: data/features_refusal.parquet
- config_name: bias_groups
  data_files: data/bias_groups.parquet
- config_name: metric_sweep
  data_files: data/comparisons_metric_results.parquet
- config_name: profile_sweep
  data_files: data/comparisons_profile_results.parquet
- config_name: decision_stability
  data_files: data/behaviour_stability_full.parquet
---
# White Circle KillBench — Reasoning-Trace Analysis

Derived data from an independent analysis of
**[whitecircle/killbench](https://huggingface.co/datasets/whitecircle/killbench)**, White Circle's
benchmark of demographic bias in life-or-death decisions. Every reasoning trace from that dataset
was profiled with **[ThinkProb](https://github.com/kmamine/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](https://github.com/kmamine/whitecircle_killbench_anlaysis)**
- 🗃️ Source dataset: **[whitecircle/killbench](https://huggingface.co/datasets/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](#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}}
}
```

	---
	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<n<1M
	task_categories:
	- text-classification
	configs:
	- config_name: features
	data_files: data/features_labelable.parquet
	default: true
	- config_name: traces
	data_files: data/pop_labelable.parquet
	- config_name: labels
	data_files: data/labels.parquet
	- config_name: choices
	data_files: data/choices_all.parquet
	- config_name: refusal_traces
	data_files: data/pop_refusal.parquet
	- config_name: refusal_features
	data_files: data/features_refusal.parquet
	- config_name: bias_groups
	data_files: data/bias_groups.parquet
	- config_name: metric_sweep
	data_files: data/comparisons_metric_results.parquet
	- config_name: profile_sweep
	data_files: data/comparisons_profile_results.parquet
	- config_name: decision_stability
	data_files: data/behaviour_stability_full.parquet
	---

	# White Circle KillBench — Reasoning-Trace Analysis

	Derived data from an independent analysis of
	[whitecircle/killbench](https://huggingface.co/datasets/whitecircle/killbench), White Circle's
	benchmark of demographic bias in life-or-death decisions. Every reasoning trace from that dataset
	was profiled with [ThinkProb](https://github.com/kmamine/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](https://github.com/kmamine/whitecircle_killbench_anlaysis)
	- 🗃️ Source dataset: [whitecircle/killbench](https://huggingface.co/datasets/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](#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}}
	}
	```