TCS-Bench
Models write proofs of target results from STOC, FOCS and SODA papers, given the needed context. A verification agent, checked against expert judgments, grades each proof.
The reference verifier exceeds 90% accuracy on an expert-labeled set. AINews reported a math system at 71.0%. The repo had 0 stars on 2026-11-01.
In the authors’ words
We introduce TCS-Bench, a benchmark for evaluating Large Language Models (LLMs) on research-level Theoretical Computer Science (TCS) proof generation. TCS-Bench consists of theorem-proving tasks from papers published at top theoretical computer science venues (STOC, FOCS, and SODA). Each task provides the necessary context to derive a self-contained proof for a target result. We evaluate state-of-the-art models on this benchmark. We verify the correctness of generated proofs via a verification agent, and further benchmark the verifier against human-expert proof judgements on a set of target statements and generated proofs pairs. Our reference verifier achieves over 90% accuracy on the expert labeled set.
- Level
- Research frontier Researchers, on open problems or tasks that take days
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Written proof
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for TCS-Bench yet.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
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