SuperLocalMemory V3: Mathematical Foundations for Production-Grade Agent Memory

SuperLocalMemory V3 applies information geometry, algebraic topology, and stochastic dynamics to AI agent memory, achieving 74.8% on LoCoMo without cloud dependency and 87.7% in full-power mode. It introduces three mathematical techniques — Fisher-Rao geodesic distance for confidence-weighted retrieval, sheaf cohomology for scalable contradiction detection, and stochastic dynamics for adaptive memory lifecycle — to solve production-scale memory problems that existing systems cannot handle. The project is open source under MIT and addresses EU AI Act compliance by keeping data local.