AI RESEARCH
PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents
arXiv CS.CL
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ArXi:2606.04780v1 Announce Type: new Persistent LLM agents require memory representations that make the formation of person understanding explicit across long term interaction. Existing agent memory methods emphasize information retention and retrieval, yet give limited account of how accumulated interaction evidence is abstracted into person understanding. We view this process as schema formation, where situated evidence is abstracted into reusable patterns and stable person level claims. We.