AutoChem: Reconstructing Chemical Reactions From Distributed Multimodal Evidence

Chemical AI systems require detailed experiment-level reaction records, yet such data remain difficult to recover from scientific literature. Information about a single experiment is often distributed across reaction figures, Supporting Information (SI), and shared procedures, connected by local compound labels and procedural references. We formulate this challenge as reaction reconstruction and introduce AutoChem, a fully automated, multimodal multi-agent framework that converts raw papers and SI into structured reaction records. AutoChem explicitly resolves compound identities and general-procedure inheritance, aligns textual and visual evidence, and preserves experiment-specific conditions, outcomes, and provenance. On 22 expert-annotated papers covering 14 reaction-information fields, AutoChem achieves 79.56% micro-F1, outperforming direct extraction with the same GPT-5-mini backbone by 32.26 points. Ablations highlight procedure inheritance as a key contributor to extraction accuracy. The reconstructed records yield a reaction-centric knowledge graph with 11,138 nodes and 39,075 edges, together with 344 controlled-variable optimization questions: 179 for condition optimization and 165 for substrate optimization. Without retrieval, none of the evaluated LLM configurations significantly exceeds its random baseline after multiple-testing correction. Cross-paper KG retrieval improves Top-1 accuracy across three models, with the largest numerical gain observed for GPT-5-mini (from 45.9% to 50.3%), suggesting downstream utility of the automatically reconstructed knowledge. Code, benchmark, and demo are available at https://anonymous.4open.science/r/AutoChem.