title: MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for
    Superior Planning and Decision-Making
summary: 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.
paper_authors._id: 66fa125293b99e4c7c87e2d3
paper_authors.name: Dayuan Fu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-30T12:42:50.223Z
paper_authors._id: 65572fd2bcdc315c0a1395d7
paper_authors.avatarUrl: /avatars/36135ca35bbd4baca37a9092f3e2d82c.svg
paper_authors.fullname: Fu-Dayuan
paper_authors.type: user
paper_authors.user: fudayuan
paper_authors._id: 66fa125293b99e4c7c87e2d4
paper_authors.name: Biqing Qi
paper_authors._id: 66fa125293b99e4c7c87e2d5
paper_authors.name: Yihuai Gao
paper_authors._id: 66fa125293b99e4c7c87e2d6
paper_authors.name: Che Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-30T12:44:06.741Z
paper_authors._id: 66d8715b36aa50556908ea7d
paper_authors.avatarUrl: /avatars/63a242ba6a4579c3a5299b1146c277c0.svg
paper_authors.fullname: Che Jiang
paper_authors.type: user
paper_authors.user: jiangche
paper_authors._id: 66fa125293b99e4c7c87e2d7
paper_authors.name: Guanting Dong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-30T12:44:00.514Z
paper_authors._id: 61cd4b833dd34ba1985e0753
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61cd4b833dd34ba1985e0753/BfHfrwotoMESpXZOHiIe4.png
paper_authors.fullname: KABI
paper_authors.type: user
paper_authors.user: dongguanting
paper_authors._id: 66fa125293b99e4c7c87e2d8
paper_authors.name: Bowen Zhou
paper_title: MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for
    Superior Planning and Decision-Making
paper_summary: 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.
paper_ai_summary: A Multi-Scale Insight Agent (MSI-Agent) enhances LLM planning and decision-making by effectively generating and utilizing task-specific and high-level insights across different scales, outperforming GPT3.5 in planning and demonstrating better robustness in domain-shifting scenarios.
paper_ai_keywords: LLMs Multi-Scale Insight Agent (MSI-Agent) experience selector insight generator insight selector domain-shifting task-specific insights high-level insights