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74.8% on the AI Memory Benchmark — No Cloud, No GPU

SuperLocalMemory V3 achieves 74.8% on the LoCoMo benchmark with data staying entirely on your device — the highest local-first score reported. 87.7% in full-power mode. 🔗 Install now: npm install -g superlocalmemory 📄 Paper: https://arxiv.org/abs/2603.14588 ⭐ GitHub: https://github.com/qualixar/superlocalmemory 🌐 Website: https://superlocalmemory.com ━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⏱️ CHAPTERS ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00 Introduction — The memory problem 0:25 Why standard memory systems fail at scale 0:55 Technique 1: Fisher-Rao Geodesic Distance 1:25 Technique 2: Sheaf Cohomology for consistency 1:55 Technique 3: Riemannian Langevin dynamics 2:20 The 4-channel retrieval architecture 2:45 LoCoMo benchmark results 3:10 Three operating modes (A, B, C) 3:30 Installation and MCP setup 3:50 Research contributions and open questions ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🧮 WHAT MAKES V3 DIFFERENT ━━━━━━━━━━━━━━━━━━━━━━━━━━━ Every AI memory system uses cosine similarity. It works. It degrades at scale. SuperLocalMemory V3 replaces heuristics with three mathematical techniques: 1. Fisher-Rao Geodesic Distance — confidence-weighted retrieval on statistical manifolds. Memories improve with use. Removing this drops multi-hop accuracy by 12 percentage points. 2. Sheaf Cohomology (H¹(G,F) = 0) — global contradiction detection algebraically. No O(n²) pairwise checking. Scales with graph size, not memory count. 3. Riemannian Langevin Dynamics — self-organizing memory lifecycle on the Poincaré ball. No hardcoded "archive after 30 days" thresholds. Provably converges to optimal state distribution. ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 LOCOMO BENCHMARK RESULTS ━━━━━━━━━━━━━━━━━━━━━━━━━━━ EverMemOS: 92.3% (cloud required, proprietary) MemMachine: 91.7% (cloud required, proprietary) SLM V3 Mode C: 87.7% (our full-power mode, MIT) Zep: ~85% (cloud required) ★ SLM V3 Mode A: 74.8% (ZERO CLOUD — data stays local) Mem0: ~64% (cloud required, $24M funded) SLM V3 Zero-LLM: 60.4% (no LLM at any stage — world first) ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔒 THREE OPERATING MODES ━━━━━━━━━━━━━━━━━━━━━━━━━━━ Mode A — Local Guardian: Zero cloud. EU AI Act compliant by architecture. 74.8% LoCoMo. Mode B — Smart Local: Mode A + local Ollama LLM. Still fully private. Mode C — Full Power: Cloud LLM synthesis. 87.7% LoCoMo. Maximum accuracy. ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 GET STARTED IN 60 SECONDS ━━━━━━━━━━━━━━━━━━━━━━━━━━━ npm install -g superlocalmemory slm setup slm remember "This project uses uv not pip" slm recall "package manager" slm dashboard Works with Claude Code, Cursor, VS Code Copilot, Windsurf, ChatGPT Desktop, Gemini CLI, and 17+ more tools via MCP. ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📚 RESEARCH & LINKS ━━━━━━━━━━━━━━━━━━━━━━━━━━━ Paper (arXiv): https://arxiv.org/abs/2603.14588 Paper (Zenodo): https://zenodo.org/records/19038659 GitHub: https://github.com/qualixar/superlocalmemory npm: https://www.npmjs.com/package/superlocalmemory PyPI: https://pypi.org/project/superlocalmemory/ Website: https://superlocalmemory.com EU AI Act page: https://superlocalmemory.com/eu-ai-act vs Mem0 comparison: https://superlocalmemory.com/alternatives/mem0 ━━━━━━━━━━━━━━━━━━━━━━━━━━━ ℹ️ ABOUT ━━━━━━━━━━━━━━━━━━━━━━━━━━━ Independent research by Varun Pratap Bhardwaj Part of Qualixar — https://qualixar.com ORCID: 0009-0002-8726-4289 MIT License. Free forever. No accounts. No telemetry. #SuperLocalMemory #AIMemory #LocalAI #OpenSource #MachineLearning #InformationGeometry #EUAIAct #ClaudeCode #CursorAI #LLM #AgentMemory #ZeroCloud #PrivacyFirst #Developer #AIInfrastructure