ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research
arXiv:2601.21008v3 Announce Type: replace Abstract: Operations Research practitioners debug infeasible models through an iterative process: inspecting Irreducible Infeasible Subsystems ( IIS), identifying constraint conflicts, and repairing formulations until feasibility is restored. Existing LLM benchmarks mostly treat OR as one-shot translation from problem descriptions to solver code, omitting this diagnostic loop. We formalize infeasible-model repair as a solver-in-the-loop Markov Decision Process in which each action triggers solver re-execution and IIS recomputation, yielding determinist
The proliferation of complex operations research problems and the rapid advancement of LLMs drive the need for more robust, autonomous diagnostic and repair capabilities in model development.
This development addresses a critical limitation in current AI applications for operations research, moving towards more autonomous and self-correcting problem-solving agents that can debug intractable problems.
The focus shifts from one-shot translation of problems to an iterative, self-correcting process using AI, fundamentally altering how complex optimization models are developed and maintained.
- · Operations Research professionals
- · LLM developers
- · Logistics and supply chain companies
- · AI agent developers
- · Manual debugging services
- · Traditional OR software vendors without AI integration
Increased efficiency and accuracy in developing and maintaining complex operations research models.
Expansion of AI agents into more sophisticated, real-world white-collar problem-solving domains.
Reduced human intervention in complex system optimization, potentially leading to fully autonomous decision-making loops in critical infrastructure.
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Read at arXiv cs.LG