RxnExBench: Benchmarking Vision-Language Models for Structured Chemical Reaction Extraction from Scientific Literature

Jiaxing Sun; Junyuan Gao; Ruikang Zhang; Hao Zheng; Lingli Ge; Xiao An; Jiang Wu; Huazheng Zeng; Jiahe Song; Chen Zhu; Haote Yang; Jingchao Wang; Lu Chen; Yuanyuan Cao; Ruijie Zhang; Shengxin Lu; Weijia Li; Lijun Wu; Conghui He

arXiv · 2610.32221

RxnExBench: Benchmarking Vision-Language Models for Structured Chemical Reaction Extraction from Scientific Literature

Jiaxing Sun·Junyuan Gao·Ruikang Zhang·Hao Zheng·Lingli Ge·Xiao An·Jiang Wu·Huazheng Zeng·Jiahe Song·Chen Zhu·Haote Yang·Jingchao Wang·Lu Chen·Yuanyuan Cao·Ruijie Zhang·Shengxin Lu·Weijia Li·Lijun Wu·Conghui He

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Abstract

Screen Tables and Substrate Scopes are information-dense figures widely used in synthetic chemistry literature, yet automated extraction of structured reaction data from them remains challenging. We introduce RxnExBench, a multimodal benchmark for fine-grained reaction-level evaluation. Built from high-impact chemistry journals, it contains 3,565 expert-annotated reactions and 2,361 molecular structures. The dataset adopts a unified schema with 15 reaction roles and 26 core fields, supporting downstream AI-for-Chemistry tasks such as retrosynthetic planning, yield prediction, and reaction condition recommendation. RxnExBench further provides a reasoning-driven difficulty taxonomy, a modular annotation pipeline, and the Oracle-MolRec protocol, which disentangles molecular recognition from higher-level reasoning. Evaluation of 9 state-of-the-art VLMs shows that performance degrades substantially as schema complexity and reasoning demands increase. Under the Oracle-MolRec setting, results further reveal that accurate molecular recognition alone is insufficient, with multimodal integration and chemistry-aware reasoning remaining the primary bottlenecks. We release all data and code to facilitate future research in chemistry-aware multimodal understanding and AI-for-Chemistry dataset construction. The dataset and code will be released at https://github.com/opendatalab/RxnExBench.

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Jiaxing Sun, Junyuan Gao, Ruikang Zhang, Hao Zheng, Lingli Ge, Xiao An, Jiang Wu, Huazheng Zeng, Jiahe Song, Chen Zhu, Haote Yang, Jingchao Wang, Lu Chen, Yuanyuan Cao, Ruijie Zhang, Shengxin Lu, Weijia Li, Lijun Wu, Conghui He. 2026-10-23. RxnExBench: Benchmarking Vision-Language Models for Structured Chemical Reaction Extraction from Scientific Literature. https://arxiv.org/abs/2610.32221

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