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MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning

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Briefings

MAMath-Bench is a process-aware benchmark evaluating multimodal mathematical reasoning in multi-agent systems. It contains 1,003 problems across 11 categories with fine-grained annotations for correctness, reasoning reliability, and robustness. The study found that multi-agent collaboration does not consistently improve final accuracy compared to strong single LLMs, but can enhance intermediate reasoning coverage while increasing redundancy. Current systems remain vulnerable to adversarial reasoning errors involving subtle structural perturbations.

Key points

• MAMath-Bench contains 1,003 problems across 11 mathematical categories with fine-grained annotations for correctness, reasoning reliability, and robustness.
• Multi-agent collaboration does not consistently improve final-answer accuracy over strong single LLMs, though it can enhance intermediate reasoning coverage.

Context

This benchmark addresses limitations in existing evaluations by focusing on multimodal reasoning, intermediate reasoning steps, and robustness against adversarial errors, unlike prior text-only single-agent studies.

Limitations

The dataset is limited to 1,003 problems across 11 categories; specific performance metrics for adversarial robustness are not fully quantified, and the study does not report long-term stability under sustained adversarial attacks.

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