Ruoyu Zhang, Zhaocen Liu, Weiyi Tian, Melissa Feng, Shilong Liu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28491 · open-access PDF
長期的なアシスタントは、情報や状況が変化するにつれて、日々未完成のタスクに従わなければなりません。情報を見つけることや行動を開始することは、タスクが完了することを保証するものではありません。我々は、現在のLLMが長期的な個人アシスタントとして短い場所を特定するために、情報の取得、判断、行動、およびシミュレート日常生活のタスクの結果を追跡するベンチマークOptionWorldを導入します。
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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