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

Researchers proposed CLEAR, an agentic framework for cross-source evidence adjudication in large language models in medicine, detailed in arXiv paper 2609.16301v1. CLEAR independently generates candidate answers from three pathways — parametric knowledge, locally curated corpora, and dynamically retrieved evidence — then uses an aggregation verifier to evaluate candidates, supporting evidence, provenance, and source-quality information for agreement and conflict. An adjudication module decides whether to preserve or revise the conclusion via override-guard and challenge-audit mechanisms, with unresolved conflicts triggering targeted follow-up search and re-adjudication.

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 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.