Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures
#graph-native #cognitive memory #AI agents #belief revision #versioned memory #formal semantics #memory architectures
📌 Key Takeaways
- Graph-native cognitive memory integrates belief revision semantics into AI agent memory systems.
- Versioned memory architectures enable AI agents to update and manage knowledge over time.
- Formal semantics provide a structured approach to handling contradictory or evolving information.
- This framework enhances AI reasoning by maintaining coherent and adaptable memory states.
📖 Full Retelling
arXiv:2603.17244v1 Announce Type: new
Abstract: While individual components for AI agent memory exist in prior systems, their architectural synthesis and formal grounding remain underexplored. We present Kumiho, a graph-native cognitive memory architecture grounded in formal belief revision semantics. The structural primitives required for cognitive memory -- immutable revisions, mutable tag pointers, typed dependency edges, URI-based addressing -- are identical to those required for managing a
🏷️ Themes
AI Memory, Belief Revision
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Original Source
arXiv:2603.17244v1 Announce Type: new
Abstract: While individual components for AI agent memory exist in prior systems, their architectural synthesis and formal grounding remain underexplored. We present Kumiho, a graph-native cognitive memory architecture grounded in formal belief revision semantics. The structural primitives required for cognitive memory -- immutable revisions, mutable tag pointers, typed dependency edges, URI-based addressing -- are identical to those required for managing a
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