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Researchers from Beihang University and Baidu propose 'constraint injection,' a dual verification method for LLM-based optimization modeling that detects spurious or omitted constraints beyond objective equivalence. They develop VRPCoder, an 8B model for translating natural-language vehicle routing problems into Gurobi scripts, achieving 93% average Pass@1 and outperforming Claude Sonnet and prior OR-LLMs by large margins.
COAgents is a cooperative multi-agent framework for solving Vehicle Routing Problems that models search as a graph, using specialized agents for node selection, move selection, and jumps to escape local minima. It achieves state-of-the-art results on CVRP and VRPTW benchmarks, reducing the gap to best-known solutions by up to 44% compared to prior learning-based methods.
This paper proposes a unified knowledge-embedded reinforcement learning framework for generalized capacitated vehicle routing problems, combining route-first cluster-second heuristics with dynamic programming to achieve superior solution quality and strong generalization across diverse variants.