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

arXiv: https://arxiv.org/abs/2604.19760 · HTML: https://mlsanity.com/paper/2604.19760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 1 of 1)

- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)

## Abstract (arXiv)

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.