Bipartite matching is a fundamental problem in game theory and market design. Classical approaches such as Gale--Shapley assume complete preferences and centralized computation, whereas many real-world matching processes are decentralized, asynchronous, and shaped by sequential interaction under limited information. We...
Wang-Xuan Fan, Xiao-Yu Nie, Zhou-Tian Shi et al.· 0 citations
With the rapid advancement of large language models (LLMs), multi-agent systems have emerged as a promising alternative to scaling up a single model. Existing approaches ensemble multiple LLMs to improve response quality, but they often rely on static prior knowledge of model capabilities and prompts, and require exten...
Jinkun Xu, Minghan Wang, Zhiyong Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Large language model (LLM) agents can benefit from reusable skills distilled from prior task experience, yet existing skill optimization methods often rely on costly execution-based evaluation and substantial task data. We introduce \textbf{COBRA-Skills}, an efficient framework that formulates skill optimization as bud...
Ping-Chen Lu, Xiang-Yi Wang, Xiang Li et al.· 0 citations
On-policy distillation (OPD) provides dense teacher supervision on student-generated trajectories, but standard reverse-KL training can assign insufficient probability to other plausible continuations. Teacher entropy alone does not reveal whether uncertainty is concentrated among a few plausible next tokens or dispers...
Zi-Kun Qu, Min Zhang, Ming-Ze Kong et al.· 4 citations
The EXPonential-weight algorithm for prompt Optimization} (EXPO) is proposed to automatically optimize the task description and meta-instruction in the meta-prompt for LLM-based agents and is extended to additionally optimize the exemplars (i.e., history of interactions) in the meta-prompt to further enhance the perfor...
Ming-Ze Kong, Zhiyong Wang, Yao Shu et al.· arXiv.org· 7 citations
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