做化学信息学或AI辅助药物研发的团队，终于有了让大模型真正看懂反应图的方法——开源框架可直接用，效果提升明显。
大模型在理解化学反应图方面存在视觉缺陷和语义脱节两大瓶颈。ChemVA框架通过视觉锚点机制和语义对齐方法，将化学结构图转化为模型能理解的实体名称，显著提升理解能力。在OCRD-Bench基准测试中，ChemVA实现92%的结构识别准确率，并在9种不同大模型上平均提升约20个百分点，使开源模型在复杂化学推理任务上媲美闭源系统。
ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding
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. To bridge these gaps, we propose the Chemical Visual Activation (ChemVA) framework, which employs a Visual Anchor mechanism to ground functional groups via hybrid-granularity detection, followed by a semantic alignment approach that translates visual features into entity names to maximize knowledge activation in LLMs. We evaluate our approach on OCRD-Bench, a newly constructed dataset featuring dense visual-semantic contexts and comprehensive reaction coverage to evaluate the full spectrum from recognition to reasoning. Extensive experiments on OCRD-Bench demonstrate that ChemVA achieves 92.0% structural recognition accuracy. By bridging visual and semantic bottlenecks, our framework delivers a consistent performance gain of approximately 20 percentage points across 9 diverse LLMs, enabling open-weight models to rival proprietary SOTA systems in complex chemical reasoning tasks.