Inference Headroom Ratio: A Diagnostic and Control Framework for Inference Stability Under Constraint

Computer Science > Artificial Intelligence [Submitted on 25 Mar 2026] Inference Headroom Ratio: A Diagnostic and Control Framework for Inference Stability Under Constraint Robert Reinertsen We present a simulation-based evaluation of the Inference Headroom Ratio (IHR), a dimensionless diagnostic quantity for characterizing inference stability in constrained decision systems. IHR formalizes the relationship between a system's effective inferential capacity C and the combined uncertainty and constraint load U + K imposed by its operating environment, and is intended to capture proximity to an inference stability boundary rather than output-level performance. Across three controlled experiments, we show that IHR functions as: (1) a quantifiable risk indicator whose relationship to collapse probability follows a well-fitted logistic curve with estimated critical threshold IHR* approx. 1.19, (2) a sensitive indicator of proximity to the inference stability boundary under environmental noise, and (3) a viable control variable whose active regulation reduces system collapse rate from 79.4% to 58.7% and IHR variance by 70.4% across 300 Monte Carlo runs. These results position IHR as a prospective, system-level complement to standard performance, drift, and uncertainty metrics, enabling estimation of remaining inferential margin before overt failure in AI systems operating under distributional shift and constraint. Comments: Resubmission with revisions addressing moderator concerns regarding distinction from signal-to-noise metrics and structural dependence in simulation design. See updated Section 4.4 for clarification Subjects: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI) Cite as: arXiv:2604.19760 [cs.AI]   (or arXiv:2604.19760v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.19760 Focus to learn more Submission history From: Robert Reinertsen [view email] [v1] Wed, 25 Mar 2026 22:21:06 UTC (98 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.SI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)