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Traditional heuristics rely on handcrafted rules for local improvements and occasional \\textit{jumps} to escape local minima, but often struggle to generalize across diverse instances. We introduce \\textbf{COAgents}, a cooperative multi-agent framework that models the search process as a graph: nodes represent solutions, and edges correspond to either local refinements or large perturbations for diversification (i.e., jumps). A \\textit{Partial Search Graph} (PSG) is dynamically constructed during search, enabling COAgents to train a Node Selection Agent and a Move Selection Agent to guide intensification, and a Jump Agent to trigger well-timed explorations of new regions. Unlike end-to-end learning approaches, COAgents cleanly separates problem-agnostic search control fr","title":"COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space","url":"https://arxiv.org/abs/2605.20618","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.20618v1 Announce Type: new \nAbstract: Although Vehicle Routing Problems (VRP) are essential to many real-world systems, they remain computationally intractable at scale due to their combinatorial complexity. Traditional heuristics rely on handcrafted rules for local improvements and occasional \\textit{jumps} to escape local minima, but often struggle to generalize across diverse instances. We introduce \\textbf{COAgents}, a cooperative multi-agent framework that models the search process as a graph: nodes represent solutions, and edges correspond to either local refinements or large perturbations for diversification (i.e., jumps). 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