ChemVA Framework Bridges LLM Gap in Chemical Diagram Understanding

Researchers have pinpointed two key obstacles hindering Large Language Models (LLMs) from accurately interpreting chemical reaction diagrams. The first is a Visual Deficit, where conventional vision encoders struggle with the complex topological connections found in dense molecular graphs. The second is a Semantic Disconnect, where typical linear representations like SMILES do not stimulate inherent chemical reasoning. To tackle these issues, they introduce the Chemical Visual Activation (ChemVA) framework, which incorporates a Visual Anchor mechanism for detecting functional groups at varying granularities and a semantic alignment strategy to convert visual features into entity names, enhancing knowledge activation in LLMs. This method is assessed using OCRD-Bench, a newly developed dataset featuring intricate visual-semantic contexts. The findings are published in arXiv:2605.17214.