Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization 事件

Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization arXiv:2604.17708v2 Announce Type: replace Abstract: 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-evolut