Early-Warning Pattern Recognition for Fail-Safe Adaptive Systems

Adaptive systems frequently fail not because they lack rules or safeguards, but because early signs of instability are detected too late, after irreversible commitments have already occurred. This paper introduces the Pattern Recognition Layer (PRL): a non-authoritative, metadata-only diagnostic layer designed to provide early warning of structural instability in phase-governed systems. PRL does not classify content, optimize outcomes, or control behavior. Instead, it detects pre-failure regimes—such as repetition without resolution, constrained pressure accumulation, and rising rupture risk—by observing phase transitions, constraint pressure, and historical coherence signals. PRL is explicitly designed to be advisory only: it may inform posture tightening but can never permit or deny action. The paper situates PRL within a Diamond integrity architecture, where generation, action, invariant reference, and closure remain the sole authorities for enforcement and recovery. By separating early pattern recognition from control, PRL enables systems to slow down, constrain themselves, and preserve identity before failure becomes unavoidable. This contribution is architectural and conceptual. No algorithms, thresholds, implementations, or performance claims are disclosed