Halder, A.; Singh, M.; Kesarwani, R.; Mathew, B.; Bhattacharya, N.; Chikhaliya, O.; Motwani, D.; Peela, S. C. M.; Samanta, S.; Muddemmanavar, P.; Farooq, M.; Ahuja, G.; Sengupta, D.

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.