Long-term memory is significant for agents, in which insights play a crucial role. However, the emergence of irrelevant insight and the lack of general insight can greatly undermine the effectiveness of insight. To solve this problem, in this paper, we introduce Multi-Scale Insight Agent (MSI-Agent), an embodied agent designed to improve LLMs' planning and decision-making ability by summarizing and utilizing insight effectively across different scales. MSI achieves this through the experience selector, insight generator, and insight selector. Leveraging a three-part pipeline, MSI can generate task-specific and high-level insight, store it in a database, and then use relevant insight from it to aid in decision-making. Our experiments show that MSI outperforms another insight strategy when planning by GPT3.5. Moreover, We delve into the strategies for selecting seed experience and insight, aiming to provide LLM with more useful and relevant insight for better decision-making. Our observations also indicate that MSI exhibits better robustness when facing domain-shifting scenarios.

翻译：长期记忆对智能体至关重要，其中洞察发挥着关键作用。然而，无关洞察的出现与通用洞察的缺乏会严重削弱洞察的有效性。为解决此问题，本文提出多尺度洞察智能体（MSI-Agent），这是一种旨在通过在不同尺度上有效总结与利用洞察，以提升大语言模型规划与决策能力的具身智能体。MSI通过经验选择器、洞察生成器与洞察选择器实现这一目标。借助这一三阶段流程，MSI能够生成任务特定及高层级的洞察，将其存储于数据库中，随后利用其中的相关洞察辅助决策。实验表明，在使用GPT3.5进行规划时，MSI优于其他洞察策略。此外，我们深入探讨了种子经验与洞察的选择策略，旨在为大语言模型提供更具实用性且更相关的洞察以优化决策。我们的观察还表明，在面对领域迁移场景时，MSI展现出更好的鲁棒性。