Paper page - OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents

Source: https://huggingface.co/papers/2605.28158

Abstract

OR-Space is a comprehensive benchmark for evaluating large language model agents in industrial operations research workflows, assessing their ability to handle persistent workspaces and multi-stage task lifecycles beyond simple text generation.

Large language model (LLM) agents are increasingly used to assist withoperations research(OR) modeling, yet existing OR-oriented benchmarks often reduce evaluation to one-shot translation from a self-contained problem statement into a mathematical formulation or solver program. Such settings abstract away two characteristics of real industrial OR workflows: persistent multi-artifact workspaces andmulti-stage task lifecycles. We introduce OR-Space, a full-lifecycle workspace benchmark for evaluating industrial optimization agents acrossmodel construction,model revision, andgrounded explanation. Each instance is an executable workspace containing business documents, structured data, optional code artifacts, solver outputs, and task-specific evaluators distributed across interdependent files. OR-Space defines threetask modes:Build, where agents construct solver-readyoptimization modelsfrom heterogeneous artifacts;Revise, where agents modify existing models under changing requirements or solver feedback while preserving valid prior logic; andExplain, where agents answer grounded questions about solutions, constraints, and business implications using evidence spread across workspace artifacts. By combiningpersistent workspaceswith lifecycle-oriented tasks, OR-Space evaluates whether agents can perform reliable optimization work beyond end-to-end text generation. We describe the benchmark design, evaluation protocol, and quality-control pipeline, and position OR-Space as a benchmark for studying the reliability, failure modes, and practical readiness of LLM agents in industrial OR workflows.

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