Organized Plasticity for Cost-Bounded Continual Adaptation

Deployed AI systems must adapt to changing inputs, environments, user patterns, and operating constraints. Sustainable adaptation cannot route every deployment failure to a single dominant learner, since many failures are local, recurrent, and cost-sensitive. This paper proposes organized plasticity, a systems-level methodology that allocates durable adaptation across distinct loci of change: input stabilization, skill compilation, memory consolidation, and executive control. This organization enables systems to stabilize poor observations before deeper inference, compile repeated successes into cheaper routines, retain reusable knowledge without broad retraining, and adjust routing or update policies without perturbing task models. A compact feasibility evaluation in a dynamic-vision setting shows that input stabilization, skill compilation, and executive control improve the accuracy-cost tradeoff in different ways while keeping the dominant learner fixed. The results motivate evaluating continual adaptation by where durable change resides, what it costs, and what collateral effects it creates, rather than by final task performance alone.