SNT-MEM: Operator-Structured Memory Management Framework

The rapid advancement of AI agents has exposed a critical bottleneck: memory management. Existing systems treat memory as passive storage, leading to unbounded growth, inefficient retrieval, and failure to consolidate knowledge across tasks. We introduce a fundamentally different perspective: memory as a closed operator-driven dynamical system. The system state evolves through a finite set of completely positive trace-preserving (CPTP) operators—fluctuation (), cyclic reset (), phase nexter (), phase reverser (), thresholding (), pruning (), and transformation ()—that together define a self-regulating mechanism. Capacity emerges not from external constraints but as a dynamical invariant from operator interactions. Memory efficiency is reinterpreted as controlled entropy flow, where stability arises from equilibrium between entropy injection (fluctuation) and reduction (pruning, compression). We evaluate SNT-MEM on long-context benchmarks with a Llama-3-8B model, comparing against vanilla RAG and Mem0-style baselines. SNT-MEM achieves 63.3% ± 2.1% memory reduction versus RAG and 22.3% ± 1.8% versus Mem0, with 3.8× ± 0.3× retrieval speedup. On LongBench multi-document QA, it achieves 4.2× token compression, 3.3× faster time-to-first-token, and bounded memory at 8GB versus unbounded baseline growth, with minimal F1 degradation ($-2.2%$). Ablation studies confirm that each operator contributes uniquely; removing any operator causes measurable performance loss, supporting functional minimality. A necessity theorem proves that any bounded memory system must implement pruning or compression. This work establishes memory as a self-organizing physical process governed by a small set of transformation rules, reframing memory from an engineering constraint to a principled dynamical system with theoretical guarantees and empirical validation on production-grade workloads.