English(EN) DAPS: Dependency-Aware Premise Selection for LLM Theorem Proving

新的DAPS系统通过利用依赖信号增强大语言模型定理证明能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了DAPS，一个新颖的依赖感知前提选择系统，旨在改进Lean 4编程语言中大语言模型（LLM）的定理证明。与依赖语义相似性且可能受流行引理影响的先前方法不同，DAPS利用数学依赖性。它利用来自Lean Blueprint项目和Mathlib依赖项的精选资源，并结合了结构化邻域编码器和分组对比目标。DAPS在Mathlib4基准测试和分布外数据集上展示了改进的性能，增强了在miniF2F和IMO-ProofBench等基准测试上的定理证明能力。 AI

影响这项研究可能显著提高大语言模型在形式数学推理方面的效率和准确性，从而可能加速人工智能在科学发现方面的能力。

排序理由该集群描述了一篇介绍大语言模型定理证明新颖系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Zixuan Chen, Wenyuan Jiang, Junling Wang, Mrinmaya Sachan, Yinya Huang · 2026-11-02 05:00

DAPS: Dependency-Aware Premise Selection for LLM Theorem Proving

arXiv:2610.41536v1 Announce Type: cross Abstract: LLM-based theorem proving in Lean 4 has advanced rapidly, but provers are difficult to leverage newly contributed lemmas, because retrieval skews toward popular foundational lemmas, leaving the right premises buried among hundreds…

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DAPS: Dependency-Aware Premise Selection for LLM Theorem Proving

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