Autonomous Geometric Spatial Indexing and Vector Alignment: A Research Framework for Zero-Memory Storage and Linear Stream Processing

This paper and accompanying codebase introduce a novel, high-performance sparse computational architecture designed to eliminate memory bloat and latency spikes in AI and spatial systems. By replacing traditional hierarchical trees (such as R-Trees) and dense memory structures with a flat, hash-backed coordinate registry, the methodology achieves true zero-RAM background overhead (0 bytes for dormant states), O(1) deterministic spot lookups via lazy state hydration, and O(N) linear vector stream alignment. Additionally, the system integrates a lightweight distributed Raft consensus engine with automated log compaction and snapshot recovery for fault-tolerant state replication.