1 citations · 1 across the 6 of their papers we have counts for
7 papers
RxnOptBench: Benchmarking LLMs for Reaction-Condition Optimization in Organic Methodology
Lingli Ge, Yubin Wang, Junyuan Gao +9
Chemical reaction-condition optimization -- choosing the catalyst, ligand, solvent, reagent, temperature, time, and atmosphere that jointly maximize yield and stereoselectivity --…
MinerU.Chem: A High-Precision System for Optical Chemical Structure and Reaction Recognition
Haote Yang, Jiang Wu, Jingchao Wang +42
In organic chemistry papers and patents, molecular structures, reaction schemes, and experimental conditions are often presented as molecular structure depictions, reaction diagram…
EditFlow3D: Automated Local Editing of 3D Assets with Trajectory Preservation
Rui Nie, Chuang Wang, Haitao Zhou +4
Controllable local editing of 3D assets requires precise target localization and appropriate visual guidance. However, existing methods lack a simple yet accurate way to obtain 3D…
Molecular Identifier Visual Prompt and Verifiable Reinforcement Learning for Chemical Reaction Diagram Parsing
Jiahe Song, Chuang Wang, Yinfan Wang +11
Reaction diagram parsing (RxnDP) is critical for extracting chemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promisin…
RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning
Jiahe Song, Chuang Wang, Bowen Jiang +13
Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machi…
Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning
Junyuan Gao, Jiahe Song, Jiang Wu +9
Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-parallel corpora, making it unclear whether…
GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation
Runchuan Zhu, Zinco Jiang, Jiang Wu +6
Refusal-Aware Instruction Tuning (RAIT) aims to enhance Large Language Models (LLMs) by improving their ability to refuse responses to questions beyond their knowledge, thereby red…