Abstract
In production systems, capability loss often hides behind polite uncertainty. Models that are technically able to provide useful, specific guidance sometimes respond with unnecessary hedging, generic deferrals, or softened language that reduces operational value. The system is not wrong. It is holding back. This paper defines that pattern as the AI Hesitation Signal (AHS) and treats it as a measurable behavioral condition rather than anecdotal user frustration. The proposed framework identifies situations where the assistant’s expressed uncertainty is disproportionate to the task context and available support. By jointly analyzing linguistic hesitation markers, response specificity, contextual risk posture, and historical capability baselines, the system computes a bounded Hesitation Risk Score. The objective is practical: surface cases where the model likely “knows enough to help” but fails to communicate with appropriate decisiveness. The architecture is model-agnostic and designed for inline deployment across enterprise copilots, customer support agents, and knowledge assistants. Field-style evaluations demonstrate strong alignment between elevated hesitation scores and real user complaints about overly cautious AI behavior. As alignment pressure increases across the industry, detecting unnecessary hesitation will become critical for preserving both productivity and calibrated trust.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "The AI Hesitation Signal: Detecting When Models Know But Don’t Say", Technical Disclosure Commons, (February 25, 2026)
https://www.tdcommons.org/dpubs_series/9395