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Shengchang Liu

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#artificial intelligence Preprint Oct 2026

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...

Sheng-Chang Liu, Ze-Xin-Yi-Chun Zhou, Zhen-Guo Xu et al. · 0 citations
Open access Jul 2026

Risk-Aware Cooperative Planning for Multiple UAVs in Non-Stationary Maritime Missions via a Scenario-Switching-Aware LinUCB Hyper-Heuristic

This study develops a hierarchical cooperative planning framework for multiple UAVs over a maritime risk field with improved reward robustness under non-stationary and high-risk profiles, rather than uniform gains across all metrics or direct field-deployment validation.

Jian Wu, Shengchang Liu, Wen-Xi Ni et al. · 0 citations

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