AI 记得你说过什么，却猜不出你的习惯
AI 助手能记住你上次说过的话，但记不住你「一直这么做」的习惯。新出的评测 HABIT-Bench 用几百轮对话模拟真实用户，测试 8 种主流记忆方案能否从零散线索推断出习惯——比如你总在周五点同一家外卖、出差时只订靠窗座位。结果最好的方案也只有 33.6% 的准确率，连把正确答案直接喂给它都提升有限。这说明「记住」和「理解」之间有条鸿沟：AI 存下了你的历史，却没能从中提炼出你的行为模式。它不是你明天能用上的东西，但提醒我们，想让 AI 真正懂你，光靠更大的记忆库远远不够。
📄 原文摘要(英文)
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 requests. We construct synthetic user histories from task-oriented dialogue data and controlled habit graphs. The histories span hundreds of sessions across food, finance, software, and travel. Four-choice tasks assess agents' ability to infer user habits and apply them appropriately. They include temporary exceptions, changes in habits, and suggestions that users have not accepted. Sampled tasks undergo independent human review. We evaluate eight memory methods with Qwen3-235B-A22B and GLM-5.3-Flash. The best-performing memory method achieves 33.61\% macro accuracy across the four domains. Performance remains limited even when annotated evidence is supplied directly. These results expose a persistent gap between access to past interactions and the ability to infer and appropriately use user habits.