From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents
Large language model-based agents have recently emerged as powerful approaches for solving dynamic and multi-step tasks. Most existing agents employ planning mechanisms to guide long-term actions in dynamic environments. However, current planning approaches face a fundamental limitation that they operate at a fixed granularity level. Specifically, they either provide excessive detail for simple tasks or insufficient detail for complex ones, failing to achieve an optimal balance between simplicity and complexity. Drawing inspiration from the principle of \textit{progressive refinement} in cognitive science, we propose \textbf{AdaPlan-H}, a self-adaptive hierarchical planning mechanism that...
| Authors | Haoran Tan, Zeyu Zhang, Chen Ma et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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