ORCAS: A CLAWs-Style Multi-Agent Framework with Routed Correction and Inspectable Representation for Optimization Modeling

Optimization modeling is a long-horizon task that relies on professional knowledge and consists of multiple independent steps, making it one of the most critical yet technically demanding tasks in operations research (OR). Large language models (LLMs) have demonstrated immense potential in automated modeling across various scenarios. Existing agentic modeling methods, relying solely on step-by-step reasoning or prompts, are unable to overcome the difficulties of ineffective collaboration among agents and lack of persistent guidance, and often fail to achieve high performance. To address these challenges, we propose ORCAS, a CLAWs-style modeling system, representing modeling competence as an externalized, inspectable state that is routable, persistent, and improvable without fine-tuning. ORCAS adopts a multilayer architecture with agent-isolated workspaces and a routed double-loop correction mechanism that classifies each detected fault and routes it to the stage at which it could have arisen, thereby achieving separation of concerns and step-level provenance while enabling modeling capability to improve with use. Experiments on eight optimization modeling benchmarks show that ORCAS achieves an average accuracy of 79.5% and surpasses state-of-the-art agentic methods, with particularly large gains on challenging tasks.