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Inferring Hidden User Models from the Behavior of Personalized LLM Agents
Recent personalized LLM agents increasingly transform information retained in memory into compressed or structured representations, which we call user models, to guide later decisions. When source wording is removed from the state reachable through the ordinary interface, these models are commonly t
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