AdaPlan-H: Self-Adaptive Hierarchical Planning for LLM Agents
A team of researchers has introduced AdaPlan-H, a self-adaptive hierarchical planning system designed for agents utilizing large language models (LLMs). Existing planning methods tend to work at a constant level of detail, which can either trivialize intricate tasks or complicate straightforward ones. Drawing from the cognitive science concept of progressive refinement, AdaPlan-H initiates with a broad macro plan and gradually hones it according to the complexity of the task. This approach creates hierarchical plans suited for different levels of difficulty, striving for an equilibrium between simplicity and complexity. The findings are available on arXiv with the identifier 2604.23194.
Key facts
- AdaPlan-H is a self-adaptive hierarchical planning mechanism for LLM agents.
- Current planning approaches operate at a fixed granularity level.
- Fixed granularity leads to excessive detail for simple tasks or insufficient detail for complex tasks.
- AdaPlan-H draws inspiration from the principle of progressive refinement in cognitive science.
- The method initiates with a coarse-grained macro plan.
- The plan is progressively refined based on task complexity.
- AdaPlan-H generates self-adaptive hierarchical plans tailored to different task difficulty levels.
- The paper is published on arXiv with ID 2604.23194.
Entities
Institutions
- arXiv