Adaptive Memory in Artificial Systems: A Constraint-Based Framework

Paper 6 interprets the constraint framework in the context of modern artificial intelligence architectures. It introduces a phase diagram describing adaptive memory systems in terms of coherence scale and plasticity rate, and shows how current architectures occupy different boundaries of this feasible region. The paper proposes that gradient-based optimization can be understood as a limiting regime of adaptive memory dynamics in which evolving structure collapses into fixed parameters, explaining both the strengths and limitations of weight-frozen neural networks.