MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for
  Superior Planning and Decision-Making
Authors: name: Dayuan Fu, name: Biqing Qi, name: Yihuai Gao, name: Che Jiang, name: Guanting Dong, name: Bowen Zhou

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.