Multi-Agent Debate and Visual Information Extraction for SeePhys Pro: A 1st-Place Technical Report from ICML 2026 AI4Math Track 3 Challenge

2026-07-24 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

The 1st-place solution for the ICML 2026 AI4Math Track 3 Challenge, "SeePhys Pro," addresses college-level physics questions where problem statements and figures can be image-based. This challenge highlights the difficulty for large language models when critical information is embedded in visual data. The winning approach employs a two-stage framework: first, a visual information extraction component translates figure content into solver-readable text, bridging the modality gap. Second, a reasoning stage coordinates three distinct solvers via multi-agent debate. Analysis revealed that orchestration primarily boosts accuracy through reliable answer selection, not extended debate, and the utility of visual aids directly correlates with the amount of problem data contained within the image. This pipeline achieved an accuracy of 0.802 on the public split, up from a 0.643 single-agent baseline, and secured 1st place on both public and private leaderboards with a private overall score of 0.743.

Key takeaway

For AI Scientists developing solutions for complex, visually-rich problems, this approach demonstrates that explicitly bridging the visual-text modality gap is critical. You should prioritize robust visual information extraction to convert image data into solver-readable text, then employ multi-agent debate for reliable answer selection. This strategy significantly enhances accuracy, especially when decisive information is embedded in figures.

Key insights

The solution effectively bridges the visual-text modality gap in physics problems using a two-stage framework and multi-agent debate for superior accuracy.

Principles

• Orchestration improves accuracy via answer selection.
• Figure aid value scales with image-locked data.
• Bridging modality gaps is crucial for LLMs.

Method

A two-stage framework: visual information extraction converts figure content to text, followed by a reasoning stage orchestrating three heterogeneous solvers through multi-agent debate.

In practice

• Implement visual-to-text conversion for image-heavy problems.
• Use multi-agent debate for robust answer selection.
• Prioritize figure analysis in visual physics tasks.

Topics

• Multi-Agent Debate
• Visual Information Extraction
• AI4Math Challenge
• Physics Problem Solving
• Large Language Models
• Modality Gap

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.