HERMES: a multi-agent framework for structured knowledge extraction from ultra-long documents in geoscience
Authoritative scientific knowledge in geoscience remains largely trapped in legacy monographs and historical literature, where unstructured text and complex layouts hinder computational access. We introduce HERMES, a scalable multi-agent framework that extracts structured data from ultra-long scientific documents. Using a coordinating large language model, HERMES integrates domain constraints, validation rules and evidence tracing within a unified document-level extraction process that incorporates parsed text, tables, figures and captions. Applied to the 55-volume Treatise on Invertebrate Paleontology, the system produced a structured database of 32,277 fossil taxonomic entities and 451,...
| Authors | Ziqi Song, Zongyuan Xiang, James G. Ogg et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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