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CLEAR is proposed, an agentic framework for cross-source evidence adjudication in LLMs in medicine that 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.
Language models can uncover latent semantic structures from one-hot training labels, but this emergent geometry is fleeting and ultimately gives way to a uniform representation.
Auto-research systems are falling into traps of "objective collapse," "validation collapse," and "acceptance collapse," mistaking closed-loop workflows for actual scientific rigor.
