Large language models (LLMs) increasingly operate as general-purpose systems that generate fluent and contextually appropriate outputs across a wide range of tasks. In deployed settings, however, many problematic behaviors do not arise from explicit errors or lack of knowledge, but from premature or misplaced commitment: the model commits to an answer or explanation even when internal evaluation is weak, unstable, or underspecified. These behaviors are often quiet, as outputs remain plausible and well-formed, making them difficult to detect or manage using conventional error-handling approaches. This article introduces the Control Probe, an inference-time control abstraction that governs when a model is permitted to commit to an output, independently of how evaluative signals are obtained. The Control Probe treats commitment admissibility as a regulated variable and enforces an explicit ordering between evaluation, inhibition, and expression. By design, this ordering prevents expression from proceeding when internal evaluation does not warrant commitment, while remaining agnostic to the specific metrics, heuristics, or learned signals used to estimate evaluative adequacy. The framework distinguishes between two forms of regulation. Type-1 regulation operates within a single inference episode, suppressing inadmissible commitment when local instability or underspecification is detected. Type-2 regulation reformulates the interaction itself to avoid recurrent instability, and requires architectural support beyond current inference interfaces. The paper defines coherence and incoherence internally in terms of commitment admissibility, rather than external correctness or calibration, and formalizes the control logic governing admissible expression. We present a concrete Type-1 implementation in a publicly available LLM and illustrate its effects using verbatim behavioral regression tests designed to surface quiet failure modes under underspecification. These examples demonstrate how admissibility gating alters inference behavior without degrading correct responses or imposing task-specific heuristics. Rather than proposing new training methods, uncertainty metrics, or safety filters, this work reframes inference as a governed process and introduces a system-level control abstraction that separates evaluation from authority. The goal is not to increase model capability, but to provide a principled mechanism for regulating commitment in settings where fluent but unsupported outputs are costly. The Control Probe offers a general lens for reasoning about inference-time behavior in contemporary LLM deployments.
Arijit Chatterjee (Fri,) studied this question.