# ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding

arXiv: https://arxiv.org/abs/2605.17214 · HTML: https://mlsanity.com/paper/2605.17214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 17 May 2026.

## Builds on (most cited first, 18 of 18)

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- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks](https://mlsanity.com/paper/2305.18365/) (2023)
- [ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area](https://mlsanity.com/paper/2408.07246/) (2024)
- [MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generation](https://mlsanity.com/paper/2205.14311/) (2022)
- [Molecular Structure Extraction From Documents Using Deep Learning](https://mlsanity.com/paper/1802.04903/) (2018)
- [OpenChemIE: An Information Extraction Toolkit For Chemistry Literature](https://mlsanity.com/paper/2404.01462/) (2024)
- [ChemMiner: A Large Language Model Agent System for Chemical Literature Data Mining](https://mlsanity.com/paper/2402.12993/) (2024)
- [GTR-CoT: Graph Traversal as Visual Chain of Thought for Molecular Structure Recognition](https://mlsanity.com/paper/2506.07553/) (2025)
- [MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs](https://mlsanity.com/paper/2601.15279/) (2026)
- [oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning](https://mlsanity.com/paper/2510.07731/) (2025)
- [Self-Labeling of Fully Mediating Representations by Graph Alignment](https://mlsanity.com/paper/2103.14133/) (2021)
- [Multimodal Search in Chemical Documents and Reactions](https://mlsanity.com/paper/2502.16865/) (2025)
- [MolMole: Molecule Mining from Scientific Literature](https://mlsanity.com/paper/2505.03777/) (2025)
- [Doc2SAR: A Synergistic Framework for High-Fidelity Extraction of Structure-Activity Relationships from Scientific Documents](https://mlsanity.com/paper/2506.21625/) (2025)

## Abstract (arXiv)

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.