MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for
  Superior Planning and Decision-Making
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.
66fa125293b99e4c7c87e2d3
Dayuan Fu
admin_assigned
2024-09-30T12:42:50.223Z
65572fd2bcdc315c0a1395d7
/avatars/36135ca35bbd4baca37a9092f3e2d82c.svg
Fu-Dayuan
user
fudayuan
66fa125293b99e4c7c87e2d4
Biqing Qi
66fa125293b99e4c7c87e2d5
Yihuai Gao
66fa125293b99e4c7c87e2d6
Che Jiang
admin_assigned
2024-09-30T12:44:06.741Z
66d8715b36aa50556908ea7d
/avatars/63a242ba6a4579c3a5299b1146c277c0.svg
Che Jiang
user
jiangche
66fa125293b99e4c7c87e2d7
Guanting Dong
admin_assigned
2024-09-30T12:44:00.514Z
61cd4b833dd34ba1985e0753
https://cdn-avatars.huggingface.co/v1/production/uploads/61cd4b833dd34ba1985e0753/BfHfrwotoMESpXZOHiIe4.png
KABI
user
dongguanting
66fa125293b99e4c7c87e2d8
Bowen Zhou
MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for
  Superior Planning and Decision-Making
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.
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.
LLMs Multi-Scale Insight Agent (MSI-Agent) experience selector insight generator insight selector domain-shifting task-specific insights high-level insights