ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding
The paper introduces ChemVA, a framework designed to enhance Large Language Models' understanding of chemical reaction diagrams. It addresses two main challenges: the Visual Deficit in interpreting molecular graphs and the Semantic Disconnect in chemical reasoning. The proposed solution shows significant improvements in structural recognition accuracy and reasoning capabilities across various models.
- ▪ChemVA aims to bridge the gap in Large Language Models' interpretation of chemical reaction diagrams.
- ▪The framework employs a Visual Anchor mechanism and a semantic alignment approach to enhance model performance.
- ▪Extensive experiments demonstrate a 20 percentage point performance gain across nine diverse LLMs.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17214 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
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| Cluster | AzonvvK5u6Q- |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.17214 (cs) [Submitted on 17 May 2026] Title:ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding Authors:Mingyang Rao, Kehua Feng, Zhihui Zhu, Jiangzhen Fu, Hao Yu, Keyan Ding, Huajun Chen View a PDF of the paper titled ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding, by Mingyang Rao and 6 other authors View PDF HTML (experimental) Abstract:While Large Language Models (LLMs) have revolutionized scientific text processing, they exhibit a significant capability gap when interpreting chemical reaction diagrams.
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