ALFM-BEM: Bidirectional Experience Memory for Continuous Learning in Foundation Model Deployments

Foundation models are deployed frozen, creating a fundamental gap: they cannot learn from deployment experiences. We introduce ALFM-BEM (Adaptive Latent Feedback Model with Bidirectional Experience Memory), a unified wrapper architecture that enables continuous learning without modifying backbone weights. The central abstraction is Bidirectional Experience Memory (BEM), a single memory structure where experiences live on a continuous outcome spectrum from failure to success. BEM naturally provides: (1) risk signals from failure similarity, (2) success patterns for retrieval-augmented reasoning, and (3) out-of-distribution detection as an emergent property of coverage. We extend the Consensus Engine with a fourth action—Query—that transforms passive abstention into active learning by requesting specific information when operating outside the experience distribution. Bounded adapters trained via experience replay enable continual improvement with provable stability guarantees. Experiments on synthetic deployment scenarios demonstrate effective failure retrieval (F1 > 0.99), strong OOD detection capability (AUC ≈ 1.0 for clustered patterns), and bounded parameter drift under continuous training. ALFM-BEM is backbone-agnostic and provides deployment infrastructure for frozen models while the fundamental architecture limitations they exhibit remain unsolved.