Modular LLM pipeline mines 27,000 papers into 8,979 curated experimental records in under an hour
bravo_abad · x · 2026-09-17
Researchers led by Li propose splitting literature extraction into a modular pipeline of specialized LLM agents instead of having one LLM read papers—because scientific papers are written for humans: compositions live in tables, conditions in prose, acronyms elsewhere, units inconsistent.
Approach and results:
- Dedicated agents: one reads tables, another extracts conditions, others expand acronyms, normalize units, reconcile names and merge records
- From 27,000+ papers they built a curated database of 8,979 experimental records with 100+ attributes
- The whole corpus processed in under an hour
Broader lesson: turn "having AI read the literature" into a repeatable engineering pipeline—modular task decomposition is the key.
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