ALFM: Adaptive Latent Feedback Model for Institutional Memory in Foundation Model Deployments

Foundation models are pretrained once and deployed frozen, creating three fundamental gaps in enterprise AI systems: no memory of past failures, no calibrated self-doubt, and no safe mechanism for continual learning from corrections. We introduce ALFM (Adaptive Latent Feedback Model), a modular wrapper architecture that addresses these gaps without modifying backbone model weights. ALFM consists of three novel components: (1) the Negative Evidence Prior (NEP), a tenant-isolated vector memory that stores failure patterns and provides risk signals at inference time; (2) the Coherence Head, a calibrated meta-model that decides when to trust, abstain, escalate, or request clarification; and (3) a three-tier adapter system (global, domain, local) enabling safe continual learning with cryptographic isolation guarantees. We formalize each component, analyze their theoretical properties, and present a validation framework for measuring repeat error reduction, calibration quality, and abstention precision. ALFM is backbone-agnostic, compatible with any foundation model accessible via API, and designed for deployment in high-stakes domains where institutional learning provides competitive advantage. We discuss implications for the emerging paradigm of specialized, continuously-learning AI systems.