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This paper introduces Xcientist, a research harness that externalizes AI-driven scientific research synthesis and validation into inspectable, contract-governed processes to ensure accountability and traceability.
This paper proposes a three-regime framework to resolve empirical contradictions in how LLMs handle conflict between training knowledge and new documents, validated across five major models. It distinguishes between parametric strength and uniqueness and demonstrates how task framing and evidence coherence significantly impact model behavior.