AI用逻辑链增强框架，解奥赛物理题接近满分
Mastering Olympiad-Level Physics with Artificial Intelligence
- 将复杂物理推理拆解为可验证的原子步骤，通过迭代修正提升准确性
- 在2025年中奥赛理论考试中获313/320分，远超人类顶尖选手
- 跨考题泛化能力强，在国际奥赛也接近满分，适合教育与科研辅助
奥赛级物理问题求解对人类和人工智能均构成重大挑战，需融合恰当建模、物理原理应用及精确计算，贯穿长链条推理过程。本文提出LOCA（LOgical Chain Augmentation）AI智能体框架，用于复杂物理推理。该框架将长推理过程分解为串行化的原子且可验证步骤，并通过增补-评审循环持续优化解法。我们在2025年中国物理奥林匹克竞赛（CPhO）理论考试这一以深度和复杂性著称的严格测试平台上评估了该框架，结果达到313/320分的接近满分表现，显著超越最高水平人类参赛者及其他基线方法。此外，LOCA在2025年国际物理奥林匹克竞赛（IPhO）考试中亦取得28.6/30的接近满分成绩，展现出优异的跨场景泛化能力。本工作为科研与教育领域可信人工智能伙伴的发展指明方向。
原文摘要 · Abstract (English)
Olympiad-level physics problem-solving significantly challenges both humans and artificial intelligence (AI), as it requires integrating appropriate modeling, application of physical principles, and precise calculation within long reasoning processes. In this paper, we introduce LOCA (LOgical Chain Augmentation), an AI agent framework designed for complex physics reasoning. LOCA decomposes long reasoning into serialized atomic and verifiable steps, refining the solution through an augment-review loop. We evaluate LOCA on the 2025 Chinese Physics Olympiad (CPhO) theory examination, a rigorous testbed renowned for its depth and complexity. The framework achieves a near-perfect score of 313 out of 320 points, significantly surpassing the top human competitor and other baseline methods. Furthermore, LOCA attains a near-perfect score of 28.6 out of 30 on the IPhO 2025 examination, demonstrating its strong generalizability across different contexts. Our work points toward the development of trustworthy AI partners in both research and education.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。