用新指标IHR评估约束环境下AI推理的稳定性风险
Inference Headroom Ratio: A Diagnostic and Control Framework for Inference Stability Under Constraint
- 提出无量纲指标IHR，量化系统推理能力与环境压力的平衡关系
- 发现当IHR低于1.19时，系统崩溃概率显著上升，呈逻辑曲线关系
- 通过调控IHR可降低79.4%的崩溃率，适合高可靠性系统监控
我们提出一种基于仿真的评估方法，研究推理余量比（Inference Headroom Ratio, IHR），这是一种用于表征受约束决策系统中推理稳定性的无量纲诊断量。IHR形式化了系统有效推理能力C与环境带来的不确定性及约束负荷U+K之间的关系，旨在捕捉系统接近推理稳定性边界的状态，而非输出性能。在三个受控实验中，我们证明IHR可作为：(1) 可量化的风险指标，其与崩溃概率的关系符合拟合良好的逻辑曲线，临界阈值估计为约1.19；(2) 环境噪声下逼近推理稳定性边界的敏感指示器；(3) 可行的控制变量，其主动调节可在300次蒙特卡洛运行中将系统崩溃率从79.4%降至58.7%，并使IHR方差减少70.4%。这些结果表明，IHR有望成为标准性能、漂移与不确定性度量的系统级补充，支持在分布偏移和约束条件下对人工智能系统失效前剩余推理裕度的估计。
原文摘要 · Abstract (English)
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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