ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding

In three linesChemVA framework advances LLM understanding of chemical reaction diagrams by addressing visual and semantic bottlenecks. Uses Visual Anchor mechanism for functional group detection and semantic alignment to activate chemical reasoning. Achieves 92.0% structural recognition accuracy on OCRD-Bench with ~20 percentage point gains across 9 diverse LLMs.

Summary generated by Claude — human-verified