Navigating the Nonlinear: The AILEE Framework for Adaptive and Resilient AI Systems

We present AILEE (AI–Load Energy Efficiency), a framework for adaptive and resilient AI systems operating under nonlinear, time–dependent conditions. The approach introduces an energy–weighted optimization metric that accumulates performance gain over time while penalizing inertia and off–resonant operation. Beyond the core metric, AILEE employs a staged trust pipeline (Safety–GRACE–Consensus–Fallback) that filters unreliable signals and guarantees bounded outputs. Illustrative examples and multi–agent extensions demonstrate how the framework integrates optimization, validation, and feedback control in a unified architecture.