Agentic Control in Variational Language Models

TL;DR

This paper explores how variational language models can incorporate internal uncertainty as a control signal for regulation, checkpointing, and inference, demonstrating improved performance and richer uncertainty profiles.

Contribution

It introduces a framework combining local variational computation, a latent regulator, and an uncertainty-aware controller for internal model control and intervention.

Findings

Variational backbone outperforms deterministic models on language tasks.

Uncertainty profiling is richer and more usable in the variational model.

Active controller yields a positive quality-cost trade-off.

Abstract

We study whether a variational language model can support a minimal and measurable form of agentic control grounded in its own internal evidence. Our model combines local variational hidden computation (EVE), a homeostatic latent regulator, structurally aware checkpoint retention and a calibrated uncertainty-aware controller operating on top of the retained model. Rather than treating uncertainty as a passive diagnostic measured after prediction, we treat it as an operational signal that can regulate training, support checkpoint retention and guide inference-time intervention. The resulting framework is deliberately focused. It studies a closed-loop form of internal control in which structural and predictive signals become actionable. Empirically, the variational backbone improves over a matched deterministic reference on the language-modeling task while also exhibiting a richer and more usable uncertainty profile. On top of this backbone, the calibrated controller remains active, uses multiple actions under a full agentic evaluation and yields a positive quality-cost trade-off. These results support a precise claim: internal uncertainty can serve not only as a descriptive property of a variational language model, but also as a practical control interface for regulation, checkpoint retention and minimal agentic routing.