AI RESEARCH
Reading Calibrated Uncertainty from Language Model Trajectories
arXiv CS.LG
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ArXi:2605.22864v1 Announce Type: new The maximum softmax probability (MSP) represents a default approach when evaluating uncertainty quantification for language model generation with structured output. Although cheap, it is often miscalibrated. Methods that probe the model's internal activations feed raw hidden states into opaque classifiers, reading activations as static snapshots and leaving implicit the layer-wise trajectory by which a representation is formed.