Author

Li-Jun Wu

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#artificial intelligence Open access Oct 2026

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

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...

Jia-Xing Sun, Jun-Yuan Gao, Rui-Kang Zhang et al. · Conference on Empirical Meth... · 0 citations

#natural language process... Preprint Sep 2026

DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents

D DianShi-RxnDB is presented, a large-scale, fine-grained organic reaction data platform built via a fully automated extraction and normalization pipeline integrating patent text, images, and reaction schemes integrating patent text, images, and reaction schemes.

Yu-Bin Wang, Xing-Jian Wei, Jiang Wu et al. · 0 citations

#artificial intelligence Review Jul 2026

InternReviewer&InternAdvocate: Objective Reward and Evaluation for Agentic Reinforcement Learning in Peer Review and Rebuttal

This work presents a comprehensive framework for the development and evaluation of specialized scholarly agents, InternReviewer and InternAdvocate, by implementing an agentic Reinforcement Learning (RL) paradigm driven by a unified objective metric and reward system.

Xuerui Su, Liya Guo, Qizhi Pei et al. · 0 citations