Abstract
Entropy-based confidence signals are increasingly leveraged to improve reasoning in large language models (LLMs), yet existing approaches treat confidence as a static quantity -- typically aggregated over tokens. We show that the *temporal evolution* of confidence during generation carries richer information than aggregate statistics alone. Analyzing token-level entropy trajectories, we identify c