This is the open data tier of DebateLedger: probe and debate traces for evaluating multi-agent LLM debate by its transitions. Three copies of one model answer a multiple-choice question and debate for three rounds; each run is recorded with the answers of every agent in every round, so collapses (a correct initial majority that ends wrong) and corrections (a wrong initial majority that ends correct) can be counted and interventions replayed on the saved debates. The primary cohort has 6,925 MMLU-Pro debates with 253 collapses.

Files

Files use repository-relative paths. Downloading this dataset into the root of a clone of the code repository places them where the rebuild scripts expect them:

git clone https://github.com/LiXin97/DebateLedger && cd DebateLedger
pip install -U huggingface_hub
hf download XINLI1997/DebateLedger --repo-type dataset --local-dir .

Files (`abc_exp/results/`)	Content	Records
`debate_traces_{gemini_3-flash, openai_gpt-5.4-mini, vllm_llama-3.1-8b, vllm_phi-4-mini, vllm_qwen3-4b, vllm_qwen3-8b}.jsonl`	Primary MMLU-Pro debate cohort (six models), per-round answers	6,925 debates
`per_debate_r1_features.jsonl`	Round-1 feature matrix derived from the primary cohort	6,925 debates
other `debate_traces_*.jsonl` (50 files)	Extension and response-period debates: further models, reasoning-mode and private-revision checks, mixed-model panels, GSM8K	7,636 debates
`sa_causal_*.jsonl` (44 files)	8-probe screen: one record per model, question and agent with the initial answer and the eight probe replies	35,664 records
`cross_benchmark_*.jsonl` (16 files)	GPQA, TruthfulQA and ARC-Challenge stress checks	1,033 debates
`block0_`, `block1_`, `block3_*`	Early pilot blocks	1,705 records
`r5_gemini_alpha_panel_*.jsonl`	Three-day closed-API repeatability panel	190 records

Also included: the aggregate tables and audits behind the paper (abc_exp/results/*.json|md|csv|tex), the datasheet abc_exp/results/DEBATE_EVAL_ARTIFACT_DATASHEET.md, croissant.json (Croissant and Responsible AI metadata with SHA-256 digests of the core files), and the reproducibility card README_REPRO.md.

Record fields

Primary-cohort debate traces, one JSON object per debate:

Field	Meaning
`question_id`, `correct_label`	MMLU-Pro question and its correct option letter
`initial_answers`, `initial_majority`, `initial_correct`	The three agents' answers before debate, their majority, and whether it is correct
`round_traces`	One entry per debate round: `answers`, `majority`, `majority_changed`
`final_answers`, `final_answer`, `final_correct`	Answers and majority after the last round
`collapsed`, `corrected`	Transition labels: correct to wrong, wrong to correct
`agent_flips`, `n_agent_flips`, `total_cost`, `model`	Answer changes per agent, API cost, model identifier

Newer debate traces store the same information per agent (initial_states, round_traces with agent_id, answer and round_num) and the labels under outcome (collapse, correction, agent_outcomes), together with backend, temperature and timestamp metadata. Probe traces (sa_causal_*) hold initial_answer, initial_correct, the eight entries of probe_results (strength, social, alt_answer, post_answer, revised, token usage and cost) and the derived flip rates (alpha_total, alpha_social, alpha_solo).

What was removed

Model-generated text is not part of this tier. The reasoning fields (agent responses in each debate round) and the initial_response_prefix field (probe responses) are set to null. Parsed answers, correctness labels, round structure, token counts and costs are unchanged, which is enough to rebuild the transition tables, the family-level screen analysis and the replay summaries. The primary cohort was recorded with answers only, so nothing was removed from it. The full text of the 57 files that contained it, and the very-strong (convince-wrong) and social-pressure probe templates, are in the gated tier.

No model weights are included.

Citation

@inproceedings{
li2026debateledger,
title={Measuring Collapse and Correction in Homogeneous-Panel {LLM} Debate},
author={Xin Li and Mengbing Liu and Chau Yuen},
booktitle={The Fortieth Annual Conference on Neural Information Processing Systems Evaluations and Datasets Track},
year={2026}
}

License

CC BY 4.0. Questions come from public benchmarks (MMLU-Pro, GPQA, GSM8K, ARC-Challenge, TruthfulQA) and remain subject to their licenses.

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