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arXiv — cs.AI

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标题为英语AIM: A Privacy-Aware Interoperable Memory Framework for Multi-Agent Multi-User LLM Systems

arXiv:2609.12320v1 Announce Type: new Abstract: Traditional large language models (LLMs) are scoped to individual user sessions, limiting their knowledge to a single conversation and preventing them from learning user preferences that evolve over time. Existing agentic memory…

arXiv — cs.AI作者: Zachary Johnson, Nigel Boachie Kumankumah, Somya Chatterjee, Tejas Sathyamurthi, Min Chen, Xinyi Alice Li, Xiao Wang, Emily Morgan Gelchie, Jessica Lin, Sadid A. Hasan, Sulaiman Vesal2026年9月14日 (9月14日)
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  • arXiv:2609.12320v1 Announce Type: new Abstract: Traditional large language models (LLMs) are scoped to individual user sessions, limiting their knowledge to a single conversation and preventing them from learning user preferences that evolve over time
  • Existing agentic memory…
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AIM: A Privacy-Aware Interoperable Memory Framework for Multi-Agent Multi-User LLM Systems

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