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SuperLocalMemory V3: Information-Geometric Foundations for Zero-LLM Enterprise Agent Memory
🤖AI Summary
Researchers introduce SuperLocalMemory V3, a new mathematical framework for AI agent memory systems using information geometry and sheaf theory. The system achieves 87.7% accuracy with cloud augmentation and offers a zero-LLM configuration that complies with EU AI Act data sovereignty requirements.
Key Takeaways
- →New mathematical foundations replace basic cosine similarity and heuristic decay methods in AI agent memory systems.
- →The system demonstrates +12.7 percentage points improvement over engineering baselines on LoCoMo benchmark.
- →A four-channel retrieval architecture achieves 75% accuracy without cloud dependency.
- →Zero-LLM configuration satisfies EU AI Act data sovereignty requirements by architectural design.
- →First work to establish information-geometric and sheaf-theoretic foundations for AI agent memory.
#ai-agents#memory-systems#information-geometry#eu-ai-act#zero-llm#data-sovereignty#benchmark#enterprise-ai
Read Original →via arXiv – CS AI
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