RxnMiner: A Multimodal Agent for Structured Reaction Data Extraction from Chemistry Figures

Reaction data underpin machine learning for reaction prediction, condition recommendation, and retrosynthetic planning, yet extracting these data from literature figures still requires substantial manual effort. Many figures encode multiple experiments through shared templates, substituent tables, and product grids. Recovering concrete reaction records therefore requires molecular recognition, association of evidence with individual experimental entries, and reconstruction of indirectly specified participants.We present RXNMINER, a multimodal agent built on Pi that combines an expert-informed extraction procedure with a chemistry toolbox to coordinate these operations. The agent reads molecular structures, resolves shared and entry-specific evidence, and instantiates participants from templates to assemble source-linked reaction records. We also introduce CHEMRXN-BENCH, a benchmark of 603 reaction figures with 8,547 manually annotated and reviewed reaction records, evaluated under a common protocol for exact molecular identity and reactant–product pairing.On this benchmark, RXNMINER raises the locally deployed Qwen3.8-Flash-Next backbone's stereochemistry-aware reaction macro-F1 from 21.0% under vanilla prediction to 71.0%, compared with 25.1% for the strongest existing extraction baseline. Gains over Bare Pi across all five evaluated backbones support the combined value of the extraction procedure and chemistry toolbox. Applied without human intervention to 10,107 literature figures, RXNMINER produces a silver corpus of 88,202 source-linked reaction records. We release the benchmark, scorer, agent artifacts, and extracted corpus to support reproducible evaluation and scalable curation of reaction knowledge.