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Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents
Large language model (LLM) agents serving different users often solve related tasks, yet separate user histories can leave reusable experience inaccessible to other agents. Pooling memories expands access but risks transferring preferences that conflict with the receiving user's requirements. We int
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