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ICML2026顶会

Editable Proof Sketch for Automated Theorem Proving

Zikai Xiao, Hanzheng Wang, Meng-Hao Guo, Shi-min Hu, Shing-Tung Yau

出版方
2026年份

摘要

As large language models (LLMs) improve in mathematical reasoning and formal understanding, a promising approach for automated theorem proving (ATP) is to enable LLMs construct proof sketches, which plan a high-level proof strategy and decompose complex theorems into independently provable subgoals. However, most existing proof sketches are immutable. As a result, any revision typically requires rebuilding the entire sketch, which discards already proved subgoals and bring additional cost. In this paper, we address this limitation by introducing EditableSketch, an editable proof-sketch structure that supports in-place edits for error correction and further subgoal decomposition while preserving previously proved subgoals. Building on EditableSketch, we introduce SketchRefine, a proof-generation framework for ATP by iteratively refining proof sketches through localized, incremental edits. Experiments show that our method not only reduces the cost of the proof process, but also achieves superior performance. For example, our method realizes 76.0% pass rate on FormalMath-Lite (+14.1% vs. DeepSeek-Prover-V2-671B). Meanwhile, compared with Hilbert, our method significantly reduces token overhead while achieving comparable performance.

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