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MACReD, a hierarchical multi-agent framework, was developed to parse complex chemical reaction diagrams by coordinating specialized agents for molecular perception, arrow understanding, text extraction, and reaction reconstruction. It uses a multigraph fusion mechanism to integrate heterogeneous cues and enforce chemically consistent global reasoning. MACReD achieves state-of-the-art performance on the RxnScribe benchmark, with F1 scores of 75.2% and 84.6% under hard and soft match criteria, demonstrating robustness across diverse diagram layouts.
Chemical reaction diagram parsing, a notoriously difficult task for vision-language models, sees a significant leap in performance thanks to a new multi-agent framework that enforces chemical consistency.
Parsing chemical reaction diagrams from scientific literature is challenging due to heterogeneous layouts, intertwined visual elements, and the difficulty of integrating recognition and reasoning. Existing vision-language models advance multimodal understanding but still fail on complex diagrams, struggling to maintain spatial coherence and to integrate multidimensional information during reasoning. To address these issues, we propose MACReD, a hierarchical multi-agent framework that coordinates specialized agents for molecular perception, arrow understanding, text extraction, and reaction reconstruction within a unified VLM-guided architecture. The planning and perception layers use flexible, fine-grained detection to handle visual complexity, while the reasoning layer uses a multigraph fusion mechanism to integrate heterogeneous cues and enforce chemically consistent global reasoning. Experiments on the RxnScribe benchmark show that MACReD achieves state-of-the-art performance, with F1 scores of 75.2% and 84.6% under hard and soft match criteria, outperforming the RxnScribe baseline, which obtains 69.1% and 80.0%, respectively. These results demonstrate the robustness of MACReD across diverse diagram layouts, including multi-step and tree-structured reactions.
