HABIT-Bench: Evaluating Habit Induction from Longitudinal Weak Evidence in Agent Memory

arXiv:2610.24520v2 Announce Type: replace Abstract: In long-term interactions with large language model (LLM) agents, users reveal habits through their choices and actions. These habits capture implicit user information that agents must infer across conversations. However, individual observations may provide only partial evidence about a habit or when it applies. Retaining past interactions alone does not ensure that agents can infer habits and apply them appropriately. We introduce HABIT-Bench to evaluate how well agent memory systems infer habits from weak evidence and apply them to later re

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