Prefill-based decode-length prediction has hard limits set by autoregressive stochasticity, not predictor design—changing the model, data, or sampling breaks existing benchmarks because they measure task difficulty, not predictor quality.
This paper reveals why token-length prediction for LLMs fails when conditions change, showing the problem isn't bad predictors but fundamental randomness in autoregressive decoding. The authors derive mathematical bounds on prediction accuracy and show that decode length follows a normal distribution determined by the model's stopping probabilities, not the predictor architecture.