AdvancedMathBench: A Benchmark Suite for Advanced Mathematical Proof Generation and Verification
Score
78该基准填补了高级数学推理评估空白，提供细粒度错误分析，信息增量显著。
论文结构完整，实验设计严谨，对模型能力的刻画全面客观。
对研究者评估和改进LLM数学推理能力有直接指导意义，但限于学术界使用。
2026年7月最新论文，紧跟当前大模型推理能力研究热点。
arXiv预印本，尚未经同行评审，但来自知名研究机构且方法扎实。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
This paper proposes the most comprehensive benchmark for advanced mathematical reasoning so far, with rigorous experimental design and reliable conclusions.
AI commentary
「What strikes me most is the fine-grained evaluation of advanced mathematical reasoning—no longer just final answers, but the proof process and error detection. For researchers aiming to push LLM reasoning boundaries, ProverBench and VerifierBench offer valuable testing grounds. Especially relevant for AI reasoning researchers and evaluation teams.」
Quick overview
AdvancedMathBench is a benchmark suite comprising ProverBench (296 problems at undergraduate and doctoral qualifying-exam levels) and VerifierBench (888 model-generated proof trajectories with expert annotations) to evaluate LLMs on advanced mathematical proof generation and verification. Experiments show that GPT-5.5 achieves only 75.8/66.1 on UGD/QE splits for generation and a Balanced F1 of 65.1 on verification, with low true negative rates, indicating critical error detection as a major bottleneck.
- Introduces AdvancedMathBench, including ProverBench and VerifierBench, covering advanced mathematics from undergraduate to doctoral qualifying-exam levels.
- Develops an automatic proof verification pipeline trained on large-scale expert annotations, showing strong agreement with human experts.
- GPT-5.5 achieves only 75.8/66.1 on UGD/QE splits for proof generation, and a Balanced F1 of 65.1 on proof verification.
- Models exhibit low true negative rates, making critical error detection a major bottleneck.
Full text
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