Coarse-to-Fine Grounded Memory for LLM Agent Planning

TL;DR

This paper introduces Coarse-to-Fine Grounded Memory, a framework that enhances LLM-based agents by grounding memories at multiple granularities, improving planning flexibility and adaptability in complex scenarios.

Contribution

It proposes a novel multi-granularity memory grounding approach for LLM agents, enabling better experience utilization and dynamic plan correction.

Findings

Improved planning flexibility in diverse scenarios.

Enhanced ability to adapt to environmental anomalies.

More effective experience retrieval for complex tasks.

Abstract

Recent advancements in Large Language Models (LLMs) have driven growing interest in LLM-based agents for complex planning tasks. To avoid costly agent training, many studies adopted memory mechanism that enhances LLM with offline experiences or online trajectory analysis. However, existing works focus on single-granularity memory derived from dynamic environmental interactions, which are inherently constrained by the quality of the collected experiences. This limitation, in turn, constrain the diversity of knowledge and the flexibility of planning. We propose Coarse-to-Fine Grounded Memory (\Ours{}), a novel framework that grounds coarse-to-fine memories with LLM, thereby fully leverage them for flexible adaptation to diverse scenarios. \Ours{} grounds environmental information into coarse-grained focus points to guide experience collection in training tasks, followed by grounding of actionable hybrid-grained tips from each experience. At inference, \Ours{} retrieves task-relevant experiences and tips to support planning. When facing environmental anomalies, the LLM grounds the current situation into fine-grained key information, enabling flexible self-QA reflection and plan correction.