Thinking-mode VLMs collapse answer-token entropy, but thinking-chain entropy and length serve as robust, zero-cost hallucination predictors.
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Temperature scaling of density-matrix eigenvalues from LLM semantic embeddings optimizes proper-score calibration and corrects systematic overconfidence so entropy equals risk.
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When Thinking Hurts: Epistemic Signals in the Reasoning Chains of Visual Language Models
Thinking-mode VLMs collapse answer-token entropy, but thinking-chain entropy and length serve as robust, zero-cost hallucination predictors.
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Eigenvalue Calibration for Semantic Embeddings of Large Language Models
Temperature scaling of density-matrix eigenvalues from LLM semantic embeddings optimizes proper-score calibration and corrects systematic overconfidence so entropy equals risk.