---
id: 20261029-T0-09
title: "自动定理证明也逃不过“苦涩教训”：h1方法靠规模而非人工流程"
title_en: "Automated Theorem Proving Meets the Bitter Lesson with h1"
url: https://ai.daily.yangsir.net/daily/20261029-T0-09
issue_date: 2026-10-29
publish_date: 2026-10-28T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2610.35962
---

# 自动定理证明也逃不过“苦涩教训”：h1方法靠规模而非人工流程

arXiv新论文探讨自动定理证明中的“苦涩教训”。非形式化数学的进展主要来自扩展前沿模型的思维链和智能体推理，而形式化定理证明则越来越依赖围绕模型的人工设计工作流，如规划。论文提出h1方法，试图用规模化的方式替代部分人工流程。对定理证明方向的研究者，这提供了一个检验“规模优先”是否同样奏效的案例。

## English Version

**Automated Theorem Proving Meets the Bitter Lesson with h1**

A new arXiv paper examines the Bitter Lesson in automated theorem proving. Progress in informal mathematics has come largely from scaling chain-of-thought and agentic reasoning in frontier models, while formal theorem proving has increasingly relied on human-designed workflows such as planning around those models. The paper introduces h1, an approach that seeks to replace some human-designed process with scale. It offers theorem-proving researchers a test case for whether scale-first strategies transfer.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2610.35962)

**详情页**：https://ai.daily.yangsir.net/daily/20261029-T0-09

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