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The paper proposes CLEAR, an agentic framework for cross-source evidence adjudication to improve large language models in medicine by handling conflicts from multiple knowledge sources. It demonstrates competitive performance across benchmarks, with significant gains in settings where direct inference or retrieval is weak.
PathoSage introduces a three-stage framework for pathology multimodal reasoning that separates knowledge retrieval, evidence collection, and evidence adjudication to reduce hallucinations and handle conflicting evidence, featuring a training-free Beta-Bernoulli experience system for modeling tool reliability.