arXiv:2605.28158v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used to assist with operations 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 and multi-stage task lifecycles. We introduce OR-Space, a full-lifecycle workspace benchmark for evaluating industrial optimization agents across model c
The proliferation of LLMs and their application in specialized domains like operations research necessitates more robust and industry-relevant evaluation benchmarks to steer development effectively.
This benchmark provides a critical tool for developing and assessing AI agents capable of solving real-world, multi-stage industrial optimization problems, accelerating their deployment and impact.
The evaluation of AI optimization agents shifts from abstract, one-shot problem-solving to full-lifecycle, multi-artifact industrial workflows, making future agent development more practical and impactful.
- · AI agent developers
- · Operations research practitioners
- · Industrial sectors (logistics, manufacturing)
- · LLM providers
- · Companies relying on outdated optimization methods
- · AI agents trained only on synthetic or simplified data
More capable and reliable AI agents will emerge for complex industrial optimization tasks.
Increased automation and efficiency gains will be realized across various industrial sectors currently bottlenecked by optimization challenges.
The enhanced performance of these agents could lead to significant competitive advantages for early adopters, potentially reshaping industry leadership in operational efficiency.
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Read at arXiv cs.AI