Inference Headroom Ratio: A Diagnostic and Control Framework for Inference Stability Under Constraint
Researchers have introduced the Inference Headroom Ratio (IHR), a diagnostic framework to characterize AI stability and predict system collapse under constraints. IHR enables the estimation of remaining inferential margins before overt failure, offering a proactive approach to AI reliability.
Computer Science > Artificial Intelligence
Title:Inference Headroom Ratio: A Diagnostic and Control Framework for Inference Stability Under Constraint
View PDF HTML (experimental)Abstract: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.
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Source: arXiv cs.AI Recent
