Inference Headroom Ratio: A Diagnostic and Control Framework for Inference Stability Under Constraint
This addresses the issue of predicting and preventing system collapse under constraints for AI systems, though it is incremental as it builds on existing stability and uncertainty metrics.
The paper tackles the problem of inference stability in constrained AI systems by introducing the Inference Headroom Ratio (IHR) as a diagnostic and control framework, showing that active regulation of IHR reduces system collapse rate from 79.4% to 58.7% and IHR variance by 70.4% in simulations.
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.