HabitusMem: Bayesian Belief Memory over Evolving User Preferences for LLM Agents

Personalized LLM agents must act on a user's current preferences, which evolve across interactions and are rarely stated in full. Existing memory systems store and retrieve content from the interaction history, leaving the agent to work out from fragments which preferences still hold and where they apply. Drawing on Bourdieu's account of habitus as durable but revisable dispositions, we model the user as a latent preference state that every interaction, including the current request, both reveals and updates. Bayesian inference over this state yields the assistant's belief about the user, and Bayesian decision theory specifies what memory should convey: the part of the belief that bears on the agent's current decision. We implement this view in HabitusMem, a supervised writer that renders the belief as text records for a frozen agent, each stating a claim with its evidence, scope, and status. The writer's training tasks mirror belief inference, belief update, and decision projection, and one writer serves both personalized question answering and personalized agent tasks. Experiments on PersonaMem-v2 and VitaBench 2.0 with two agent families show that HabitusMem outperforms existing memory systems in personalized question answering and yields the most reliable task completion for personalized agents among all non-privileged conditions, surpassing even full-history access.