用多智能体辩论和图像信息提取解决图像物理题，准确率提升至80.2%。
Multi-Agent Debate and Visual Information Extraction for SeePhys Pro: A 1st-Place Technical Report from ICML 2026 AI4Math Track 3 Challenge
- 先将图像中的物理信息转为可读文本，再用三个不同智能体辩论求解。
- 准确率从0.643提升至0.802，私榜也达0.743，获第一。
- 图像越关键，辅助作用越明显，核心优势是选答案而非争论过程。
本文介绍我们在第三届人工智能与数学研讨会（AI4Math）SeePhys Pro挑战赛第3赛道的解决方案。该任务要求回答大学水平的物理问题，题干和图示可能以图像形式呈现。当关键信息位于图像中时，大语言模型面临严重模态鸿沟，且信息越依赖图像，难度越高。我们提出两阶段框架：第一阶段通过视觉信息提取将图像内容转化为可被求解器理解的文本，以弥合模态差距；第二阶段通过多智能体辩论协调三个异构求解器进行推理。分析发现：协作增益主要来自可靠答案选择，而非额外辩论；图像辅助的价值随问题中图像信息占比增加而上升。最终系统在公开测试集上准确率从单智能体基线0.643提升至0.802，在公开与私有排行榜均获第一（私有整体0.743）。
原文摘要 · Abstract (English)
This technical report presents our approach to Challenge Track~3: SeePhys Pro at the 3rd AI for Math Workshop, where the task is to answer college-level physics questions whose statement and figure may be given partly or entirely as an image. Visual physics problems become substantially harder for large language models when the decisive information resides in a figure rather than in the text, and this modality gap widens as more of the problem migrates into the image. We address the task with a two-stage framework: a visual information extraction stage that re-expresses figure content as solver-readable text to close the modality gap, and a reasoning stage that orchestrates three heterogeneous solvers through multi-agent debate. Our analysis yields two findings: the gain from orchestration comes from reliable answer selection rather than from additional debate, and the value of a figure aid scales with how much of the problem is locked inside the image. The resulting pipeline improves overall accuracy over a single-agent baseline from 0.643 to 0.802 on the public split, and won 1st place on both the public and the private leaderboard (private overall 0.743).
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