AI RESEARCH
ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding
arXiv CS.CL
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ArXi:2605.17214v1 Announce Type: cross While Large Language Models (LLMs) have revolutionized scientific text processing, they exhibit a significant capability gap when interpreting chemical reaction diagrams. We identify two fundamental bottlenecks restricting current systems: a Visual Deficit, where generic vision encoders struggle to resolve the strict topological connectivity of dense molecular graphs, and a Semantic Disconnect, where standard linear strings, such as SMILES, fail to effectively activate the model's latent chemical reasoning.