RAG++: Memory-Conditioned Candidate Selection with Trajectory-Aware Attention

Retrieval-Augmented Generation (RAG) systems typically treat retrieved context as a flat collection of documents, ignoring the structural and temporal relationships between conversation turns. We present RAG++, a trajectory-aware retrieval system that positions memories in a 5-dimensional coordinate space (depth, sibling order, homogeneity, temporal position, and complexity) and enforces context admissibility through cryptographically-verified slicing. Our system introduces three key innovations: (1) **Inverse Ring Contextual Propagation (IRCP)**, an attention mechanism that propagates information in both causal and anti-causal directions through ring topology; (2) **Slice-Conditioned Retrieval**, where a separate Graph Kernel service serves as the sole admissibility authority for context selection; and (3) **Conservation Metrics**, mathematical invariants that ensure bounded forgetting in memory systems. We consolidate three previously separate attention mechanisms (IRCP, RCP, TPO) into a unified architecture, achieving a 92% code reduction (42K to 3.35K LOC) while maintaining functional equivalence. Benchmarks demonstrate p95 latency of 8.1ms, throughput of 12.5k QPS, and successful scaling to 150M vectors. **Keywords**: Retrieval-Augmented Generation, Trajectory Memory, Context Slicing, Attention Mechanisms, Vector Search