Predictive Latents for Autoregressive Language Models: Possibility, Gauge, and Obstruction

Yuzhou Hong · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28540 · open-access PDF

Abstract

Autoregressive language models are trained by next-token cross-entropy, yet several recent systems also iterate a hidden state without emitting a token: continuous thoughts, pause steps, and recurrent depth. This note separates three risks on one latent process. The token risk $R_{\mathrm{tok}}$ scores a readout. The teacher-forced latent risk $R_1$ scores a one-step map that sees the true hidden state. The open-loop risk $R_H$ scores the same map after it has been iterated on its own outputs. If the Bayes next-token posterior is a function of a $k$-dimensional statistic with full-rank covaria

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