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Reasoning validity in LLMs can be more accurately assessed through a novel three-stream detector that integrates motion with contextual state information, achieving up to 21% better accuracy than existing methods.
Detector rankings can flip dramatically across domains, challenging the reliability of benchmark-based selections for zero-shot OOD detection.
Representation trajectories can provide a critical reliability signal that enhances OOD detection and classification performance, outperforming traditional final-state approaches.
