license: cc-by-nc-sa-4.0
language:
- en
pretty_name: ProofRank Annotated Examples
size_categories:
- n<1K
tags:
- mathematics
- proof-evaluation
- llm-as-a-judge
- pairwise-comparison
- qualitative-analysis
configs:
- config_name: default
data_files:
- split: train
path: data/examples.jsonl
ProofRank: Annotated Pairwise Comparison Examples
We include eight pairs of LLM-written solutions from the human annotation study of ProofRank, each with the problem, both solutions, the annotators' verdicts, and a commentary for the case studies given in the paper:
clear_cut_1--clear_cut_4: pairs on which both annotators independently reached the same verdicts.contentious_1--contentious_4: pairs on which the annotators split, or on which the LLM judge (GPT-OSS-120B) disagreed with them.
Files
| File | Content |
|---|---|
data/examples.jsonl |
One row per example (fields below); this is the machine-readable version. |
examples_agreement.md |
The four clear-cut examples, rendered as markdown. |
EXAMPLES.md |
The four contentious examples, rendered as markdown. |
| Column | Type | Description |
|---|---|---|
example_id |
string | clear_cut_<n> or contentious_<n>. |
group |
string | clear_cut or contentious. |
title |
string | Short description of what the example illustrates. |
problem_id |
string | Problem identifier in ProofRank. |
solution_1_model, solution_2_model |
string | Models that wrote the two solutions. |
content_markdown |
string | Problem, both solutions, annotator verdicts and commentary, as markdown. |
Licensing
The problems are drawn from publicly available mathematics competitions and previously released benchmarks, and the human-written reference solutions are collected from public Art of Problem Solving posts and official competition materials. We abide by the sources' terms and conditions and redistribute this material under CC BY-NC-SA 4.0 - see LICENSE - for non-commercial research use and with proper credit to the original competitions.
The terms of the original sources continue to apply to the redistributed text, independently of that license. Namely, for official competition problems and previously released benchmarks, they typically in the public domain or released under comparable conditions, which likewise forbid use of the material for training.
Accordingly, this data must not be used to train any AI model. Users are responsible for complying with the original terms that apply to each entry.
Work produced by us - code, results, and evaluation scripts - is published under the more permissive MIT license.
Citation
@inproceedings{petrov2026proofrank,
title={Not All Proofs Are Equal: Evaluating {LLM} Proof Quality Beyond Correctness},
author={Petrov, Ivo and Dekoninck, Jasper and Dimitrov, Dimitar I. and Vechev, Martin},
booktitle={The Fortieth Annual Conference on Neural Information Processing Systems Evaluations and Datasets Track},
year={2026},
url={https://openreview.net/forum?id=Zf9xKiTAom}
}