5 papers
Rethinking Self-Evolving Agent Skills: Feedback Dynamics over Multiple Rounds
Yuxuan Liu, Zhaochen Su, Yuhao Zhang +9
Self-evolving skill systems promise to improve agents by turning execution feedback into persistent skill updates without changing the underlying model. Yet it remains unclear when…
SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision
Yuxuan Liu, Zhaochen Su, Lingyun Xie +11
Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving methods refine skills using…
PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization
Federico Berto, Chuanbo Hua, Laurin Luttmann +6
Combinatorial optimization problems involving multiple agents are notoriously challenging due to their NP-hard nature and the necessity for effective agent coordination. Despite ad…
Multi-Action Self-Improvement for Neural Combinatorial Optimization
Laurin Luttmann, Lin Xie
Self-improvement has emerged as a state-of-the-art paradigm in Neural Combinatorial Optimization (NCO), where models iteratively refine their policies by generating and imitating h…
Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding
Laurin Luttmann, Lin Xie
The Mixed-Shelves Picker Routing Problem (MSPRP) is a fundamental challenge in warehouse logistics, where pickers must navigate a mixed-shelves environment to retrieve SKUs efficie…