ARCagent: An Adaptive Retrieval Calibration Agent for Clinical Question Answering

Abstract:In diseases where clinical guidelines are incomplete, contested, or mutually contradictory, knowledge completeness and dynamic conflict-aware synthesis are two safety-critical properties that standard Retrieval-Augmented Generation systems do not provide. Therefore, we present \sysname, an adaptive retrieval calibration clinical question-answering agent for ME/CFS, a disease where diagnostic frameworks coexist and major guidelines actively contradict each other on treatment. ARCagent contributes three components. First, a 1,706-chunk, 10-source knowledge base with a structured inter-guideline conflict registry spanning all active ME/CFS diagnostic frameworks. Second, a conflict-aware retrieval calibration pipeline that re-ranks retrieved evidence using query-specific focus and conflict signals. Third, a benchmark scored by LLM-as-Judge, avoiding systematic underestimation averaging 10.1 percentage points caused by keyword matching. ARCagent achieves 95.3%, outperforming all base LLMs. Code is available at this https URL.

Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

arXiv:2609.36392 [cs.AI]

(or arXiv:2609.36392v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.36392 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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