arXiv AI
2d ago

On the Fundamental Limits of Prefill-Based Decoding Length Prediction

The paper investigates the limits of predicting how many tokens a large language model will generate using only prefill-phase hidden states. It shows that current benchmarks mix predictor quality with task difficulty, leading to unstable accuracy when models, datasets, or sampling settings change. By modeling decoding as a stopped stochastic process, the authors derive bounds and an affine variance law that explain the observed normality of per‑prompt decode lengths and confirm that system error dominates model error.

By Haowen Shi, Xuzhe Yan, Fahao Chen, Peng Li
arXiv Machine Learning
Oct 12

Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes

The paper introduces a two-way guidance framework that jointly learns summary statistics and posterior distributions for Bayesian simulation-based inference. By imposing a structural restriction on the learned summary, the method ensures that the posterior remains sufficient, while the posterior objective simultaneously steers the summary toward sufficiency. The authors provide theoretical guarantees on posterior estimation error and the deviation of the learned summary from sufficiency, outlining conditions on simulation budgets needed to recover increasingly concentrated posteriors as observation size grows.

By Percy S. Zhai, Max Pachman, Peverel D. Shipley, Jungeum Kim