Operator-Theoretic Memory System A research prototype for long-context and streaming NLP, modeling language memory as a continuous operator in Hilbert space. The approach enables stable token-level updates, selective forgetting of irrelevant linguistic information, and efficient long-horizon language sequence modeling, with theoretical guarantees via Lyapunov stability. Includes PyTorch implementations and comparisons with RNN and Transformer architectures.