SuperLocalMemory V3: Mathematical Foundations for Production-Grade Agent Memory

We applied information geometry, algebraic topology, and stochastic dynamics to AI agent memory. 74.8% on LoCoMo with data staying local — the highest score reported without cloud dependency. 87.7% in full-power mode. 60.4% with no LLM at any stage. Open source under MIT. The Problem Is Scale, Not Storage Every AI coding assistant — Claude, Cursor, Copilot, ChatGPT — starts every session from scratch. The memory problem has been solved at development scale: Mem0, Zep, Letta, and others provide memory layers that work well for individual developers and small teams. The unsolved problem is what happens at production scale. At 10,000 memories, cosine similarity stops discriminating between relevant and irrelevant results. At 100,000 memories, contradictions accumulate silently — "Alice moved to London" and "Alice lives in Paris" coexist without detection. At enterprise scale, hardcoded lifecycle thresholds ("archive after 30 days") break because usage patterns vary across teams, projects,