Ruoyu Zhang, Zhaocen Liu, Weiyi Tian, Melissa Feng, Shilong Liu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28491 · open-access PDF
长期助理必须每天跟踪未完成的任务,因为信息和情况发生变化。 但是,寻找信息或开始行动并不能确保任务完成。 我们引入了OptionWorld,一个基准,在模拟日常生活任务中追踪信息收集,判断,行动和结果,以确定当前LLM在长期个人助理中短缺的位置。
Machine translation (Meta M2M100); the English original follows.
Long-term assistants must follow unfinished tasks across days as information and circumstances change. Finding information or starting an action, however, does not ensure that the task gets completed. We introduce OptionWorld, a benchmark that traces information acquisition, judgments, actions, and outcomes in simulated daily-life tasks to identify where current LLMs fall short as long-term personal assistants. In runs with archived decision inputs, the key facts remain in the model's last input before the deadline in 35.5% of failed instances. Failure analysis identifies the three most freque
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