We describe a stability framework for detecting, and in certain settings correcting, system degradation across heterogeneous physical and computational domains. The core element is a three-term stability metric, Φ = I × ρ − α × S, where I (Identity) measures functional preservation relative to a known-good baseline, ρ (Coherence) measures short-horizon temporal consistency, and S (Entropy) measures output disorder. The structural form of the metric is fixed; only the domain-specific definitions used to compute I, ρ, and S vary with the available signals. We evaluate the metric across eight domains: mechanical bearings, turbofan engines, power grids, geophysical systems, neural network training, quantum circuits on IBM hardware, physiological cardiac signals, and large language models. Across these settings, Φ decreases in advance of observed failures or performance collapse, providing early-warning behavior without requiring a learned predictor trained on failure labels. We also present the Φ-Objective Controller, an autonomous control engine that consumes Φ from a domain adapter, forecasts the effect of candidate interventions using a lightweight surrogate model, and selects actions that maximize expected task performance subject to a Φ-based safety constraint. To mitigate proxy-gaming failure modes, the controller implements three anti-Goodhart safeguards that reject interventions when the metric improves while measured task performance is predicted to worsen. The methods described in this paper are the subject of fourteen U.S. provisional patent applications filed between October 2025 and February 2026, including U.S. Provisional Application No. 63/984,704 (filed February 17, 2026). No license to implement or commercialize the described methods is granted by this publication. All rights reserved.
Shawn Barnicle (Wed,) studied this question.