SNT-MEM: Operator-Structured Memory Management Framework For Self-Regulating AI Systems

Purpose: The rapid advancement of AI agents has exposed a fundamental bottleneck: memory management. Existing systems treat memory as passive storage, leading to unbounded growth, inefficient retrieval, and failure to consolidate knowledge. We introduce SNT-MEM, a framework that models memory as a closed operator-driven dynamical system. Methods: We formalize the memory state as a density operator (t) evolving under a closed composition of seven completely positive trace-preserving (CPTP) operators: fluctuation (), cyclic reset (), phase nexter (), phase reverser (), liminal (), irreversible loss (), and subspace mapping (). The CPTP formalism is employed not for physical quantum simulation but to enforce closure, composability, and stability under operator composition. In deployment, each operator is approximated by a corresponding graph-based operation on utility-weighted memory nodes. Simulations run over T = 1000 steps, D = 1024 memory slots, N = 50 independent runs (seed base 42) ; all reported values are raw simulation outputs with no post-hoc rescaling. Results: SNT-MEM achieves 63. 3\% 2. 1\% memory reduction versus vanilla RAG (final normalized mass 0. 661 0. 021 vs. 1. 800) and 52. 8\% 1. 7\% reduction versus Mem0-style baselines. Real-world integration with Llama-3-8B on LongBench shows 3. 3 latency reduction (46 3. 1 ms vs. 145 12. 4 ms) and 39\% token reduction while preserving competitive F1 (47. 1 1. 9 vs. 48. 2 2. 1). Ablation studies confirm functional minimality across all seven operators. Conclusion: These results suggest that the operator-interaction view of memory management warrants further investigation as a principled alternative to heuristic approaches. Limitations, failure modes, and generalization scope are discussed explicitly.