AI RESEARCH
Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization
arXiv CS.AI
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ArXi:2604.17708v1 Announce Type: new Automating operations research (OR) with large language models (LLMs) remains limited by hand-crafted reasoning--execution workflows. Complex OR tasks require adaptive coordination among problem interpretation, mathematical formulation, solver selection, code generation, and iterative debugging. To address this limitation, we propose EvoOR-Agent, a co-evolutionary framework for automated optimization.