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

arXiv cs.AISeptember 16, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2609.16301v1 Announce Type: new 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 pat

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