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DTC is introduced, a framework that estimates confidence by counting tokens at which two models strongly disagree during decoding, and finds that their count is almost negatively associated with answer accuracy, thereby serving as a simple yet effective signal for uncertainty quantification.
CADER (Confidence-Aware Dynamic Evidence Reasoning), a training-free framework for adaptive and reliable long-video reasoning, achieves competitive performance against specialized tool-augmented frameworks, suggesting a practical inference-time route for adaptive long-video reasoning.
Long-term language-model agents rely on external memory across interactions. Atomic memories are particularly useful: their fine-grained semantic boundaries enable precise retrieval and direct comparison between observations. Yet accumulating atoms inevitably become redundant, overlapping, or conflicting. Existing meth...
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