Shaopu Wang

LeafSkill: Efficient Leaf-Level Action Grounding for Recursive LLM Agents

Recursive large language model (LLM) agents solve long-horizon tasks through decomposition, execution, and environmental feedback. Yet after producing an atomic subtask, they often invoke the same large model again to translate a familiar instruction into an executable action. We observe that high-level planning is tas...

LSK-RM: A Physics-Guided Large Selective Kernel U-Net for Radio Map Reconstruction

Radio map construction aims to infer dense received-power fields from environmental layouts, sparse observations, and physical priors. While crucial for environment-aware wireless systems, it remains challenging in complex urban scenes with building-induced non-line-of-sight (NLOS) shadows and dynamic blockages. Non-it...

Hierarchical multi-policy adversarial reinforcement learning