TCSExam 基准发布:覆盖理论计算机科学全领域评测前沿 LLM
TCSExam: Evaluating Frontier LLMs on the Full Range of TCS Research
AI 导读
清华大学姚班团队等发布 TCSExam 基准与统一框架,覆盖理论计算机科学(TCS)研究全范围,评测定理证明、求解器工程、构造及开放猜想求解四类任务。TCSExam-Full 含 100 个专家评审任务,提交由 Lean 内核或可执行检查器核验;Lite 版选 20 个任务,将尝试次数减少 80%。
正文 · 原文
Abstract:Can large language models (LLMs) advance theoretical computer science and resolve its long-standing open conjectures? To study progress toward this goal, we introduce TCSExam, a unified framework and benchmark covering the full range of TCS research. It evaluates theorem proving, solver engineering, and constructions alongside direct attempts to resolve open conjectures, capturing both concrete research accomplishments and progress toward long-standing research goals. TCSExam-Full contains 100 expert-reviewed tasks, with submissions checked by the Lean kernel or an executable task checker. TCSExam-Lite selects 20 tasks, five per track, reducing the number of task attempts by 80% at the same per-attempt budget. We evaluate eight agents on Full and nine on Lite. The TCSExam Index combines all four tracks: GPT-6 Astra leads Full (71.9), and Opus 5.5 leads Lite (72.3). Full and Lite rankings are strongly aligned across eight shared agents (Spearman's $\rho=0.93$), supporting Lite as a compact, discriminative benchmark. The evaluations yield improved solver outputs and mathematical constructions, together with independently verified special cases and bounds from open-conjecture attempts. TCSExam provides an extensible framework with task sources, Docker build recipes, and evaluation tools for reproducible use.
| Comments: | 52 pages, 15 figures. Project website: this https URL Code: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.07962 [cs.AI] |
| (or arXiv:2611.07962v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07962 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yifan Luo [view email]
[v1]
Thu, 5 Nov 2026 12:58:43 UTC (956 KB)
来源:arXiv cs.AI · arxiv.org