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New DAPS system enhances LLM theorem proving by leveraging dependency signals

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DAPS, a new dependency-aware premise selection system designed to improve Large Language Model (LLM) theorem proving in the Lean 4 programming language. Unlike previous methods that rely on semantic similarity and can be skewed by popular lemmas, DAPS leverages mathematical dependencies. It utilizes curated resources from Lean Blueprint projects and Mathlib dependencies, incorporating a structural neighborhood encoder and a group-level contrastive objective. DAPS has demonstrated improved performance on Mathlib4 benchmarks and out-of-distribution datasets, enhancing theorem proving capabilities on benchmarks like miniF2F and IMO-ProofBench. AI

IMPACT This research could significantly improve the efficiency and accuracy of LLMs in formal mathematical reasoning, potentially accelerating AI's capabilities in scientific discovery.

RANK_REASON The cluster describes a new research paper introducing a novel system for LLM theorem proving. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New DAPS system enhances LLM theorem proving by leveraging dependency signals

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The cluster describes a new research paper introducing a novel system for LLM theorem proving. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zixuan Chen, Wenyuan Jiang, Junling Wang, Mrinmaya Sachan, Yinya Huang ·

    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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