CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicine

Abstract:Medical knowledge evolves continuously, whereas the parametric knowledge encoded in large language models (LLMs) is fixed at training time. External retrieval, including retrieval-augmented generation (RAG), can provide access to newly available evidence, but retrieved information may be irrelevant, incomplete, or conflicting. As a result, external retrieval can in turn degrade the factual accuracy and evidence grounding of LLM outputs. To address this challenge, we propose \textbf{CLEAR}, an agentic framework for cross-source evidence adjudication in LLMs in medicine. CLEAR independently generates candidate answers from three complementary pathways---parametric knowledge, locally curated corpora, and dynamically retrieved evidence---reflecting three common sources of information available to LLMs. An aggregation verifier jointly evaluates the candidates, supporting evidence, provenance, and source-quality information to identify agreement and conflict across sources. An adjudication module then determines whether the current conclusion should be preserved or revised through complementary override-guard and challenge-audit mechanisms, while unresolved conflicts trigger targeted follow-up search and re-adjudication.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.16301 [cs.AI]

(or arXiv:2609.16301v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.16301 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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