Deterministic retrieval recovers biomedical associations lost by language models
Journal:
bioRxiv
Published Date:
Apr 29, 2026
Abstract
Large language model (LLM)-based retrieval systems miss biomedical associations through output truncation, synonym mismatch and run-to-run variability, but the magnitude of this loss remains unclear. We present BioChirp, an open-source framework that uses LLMs for query interpretation and candidate filtering, combining multi-source consensus entity resolution with deterministic graph-based retrieval. Across four major biomedical databases, BioChirp recovered more associations with higher reproducibility than conventional LLM-based retrieval approaches.
