Solving RAG Degradation via Semantic Phagocytosis and Synaptic Reinforcement in Deterministic Graph Memory

Long-running conversational AI agents inevitably succumb to contextual degradation. As flat Retrieval-Augmented Generation (RAG) databases scale, the semantic space becomes oversaturated, burying critical historical data in "semantic noise." This document introduces the v3 Memory Pipeline of LiNDA OS, presenting a comprehensive, biologically-inspired cognitive architecture. To mitigate real-time compute overhead, the system first employs an asynchronous Cognitive Router that bifurcates volatile conversational data into immediate biographical buffers and deep relational storage. Crucially, this ingestion framework utilizes Relational Self-Modeling, maintaining a dual-node identity where the AI autonomously tracks its own execution history alongside user facts. Finally, to eliminate long-term RAG degradation, the pipeline transmutes stored vectors into a deterministic Knowledge Graph governed by Semantic Phagocytosis and Synaptic Reinforcement. Under the paradigm that "Synapses age, while Neurons do not," unused cognitive noise is autonomously transitioned into a subconscious latency without data deletion, while frequently accessed paths are strengthened. Coupled with explicit Anchor Immunization, this architecture achieves a permanent, zero-deletion contextual memory capable of infinite scaling.