ChemLabs on ChemO: A Multi-Agent System for Multimodal Reasoning on IChO 2025

• URL: http://arxiv.org/abs/2511.16205v1
• Date: Thu, 20 Nov 2025 10:15:39 GMT
• Title: ChemLabs on ChemO: A Multi-Agent System for Multimodal Reasoning on IChO 2025
• Authors: Xu Qiang, Shengyuan Bai, Leqing Chen, Zijing Liu, Yu Li,
• Abstract summary: ChemO is a new benchmark built from the International Chemistry Olympiad (IChO) 2025.<n>ChemLabs is a hierarchical multi-agent framework that mimics human expert collaboration.<n>Our top configuration achieves a score of 93.6 out of 100, surpassing an estimated human gold medal threshold.
• Score: 10.434011696348561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Olympiad-level benchmarks in mathematics and physics are crucial testbeds for advanced AI reasoning, but chemistry, with its unique multimodal symbolic language, has remained an open challenge. We introduce ChemO, a new benchmark built from the International Chemistry Olympiad (IChO) 2025. ChemO features two key innovations for automated assessment: Assessment-Equivalent Reformulation (AER), which converts problems requiring visual outputs (e.g., drawing molecules) into computationally tractable formats, and Structured Visual Enhancement (SVE), a diagnostic mechanism to disentangle a model's visual perception capabilities from its core chemical reasoning. To tackle this benchmark, we propose ChemLabs, a hierarchical multi-agent framework that mimics human expert collaboration through specialized agents for problem decomposition, perception, reasoning, and auditing. Experiments on state-of-the-art multimodal models demonstrate that combining SVE with our multi-agent system yields dramatic performance gains. Our top configuration achieves a score of 93.6 out of 100, surpassing an estimated human gold medal threshold and establishing a new state-of-the-art in automated chemical problem-solving. ChemO Dataset: https://huggingface.co/datasets/IDEA-AI4SCI/ChemO

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