OR-Space: A Workspace-Based Benchmark for Industrial Optimization Agents

Large language model (LLM) agents are increasingly used for operations research (OR) modeling, but existing OR benchmarks mostly test model construction from a self-contained problem statement. Industrial optimization also requires connecting requirements to data and code, revising models, and explaining solver results. We introduce OR-Space, a benchmark of executable workspaces containing business documents, structured data, code, solver outputs, and task-specific evaluators. OR-Space evaluates three tasks separately: BUILD, where agents create solver-ready models from heterogeneous artifacts; REVISE, where they update models under changed requirements while preserving valid prior logic; and EXPLAIN, where they ground answers about solutions and constraints in workspace evidence. OR-Space evaluates 16 language models on 100 base problems instantiated as 300 task instances, revealing substantial failures in schema mapping, constraint grounding, legacy-code use, and solver-backed explanation. Controlled studies show that workspace interface, revision context, and solver backend affect performance.