Early warning of a training-time collapse has so far been read from a model’s internals. This paper shows the same precursor is legible in the model’s own output distribution: per-token entropy variance and lag-1 autocorrelation, computed from the log-probabilities any inference…
Raising the temperature does not make a language model more creative, and this paper shows why at the level of the token race: temperature collapses value and coherence without ever raising novelty, while prompts that change which continuations can win raise judged novelty monoto…
Large language models frequently possess the knowledge needed to answer a question correctly yet commit to the wrong response. This paper presents friction-guided inference, a calibrated inference-time pipeline that uses the model's own logprob distribution — available at zero co…