arXiv:2606.19787v1 Announce Type: new Abstract: Large language models are increasingly deployed as autonomous agents for multi-step tasks in executable environments, yet their ability to perform realistic operations research (OR) work remains unclear. Existing OR evaluations often decouple modeling from solving, rely on pre-formalized or text-only instances, and rarely test the full workflow from operational artifacts to validated decisions. In this work, we introduce ORAgentBench, an execution-grounded benchmark for evaluating autonomous agents on challenging end-to-end operations research ta
The increasing sophistication of large language models and the push towards autonomous agents make evaluating their capabilities in complex, real-world tasks like operations research a timely necessity.
This benchmark directly addresses the capability of AI agents to perform end-to-end, multi-step operations research, which is critical for automating complex white-collar workflows and strategic decision-making across industries.
The introduction of ORAgentBench provides a standardized, execution-grounded method to assess LLM agents, moving beyond theoretical benchmarks to practical application and validation in operations research.
- · AI agent developers
- · Logistics and supply chain companies
- · Operations research software industry
- · Routine OR consultants
- · Companies slow to adopt AI agent technologies
Increased development and refinement of AI agents specifically designed for complex operational tasks.
Significant efficiency gains and cost reductions in sectors heavily reliant on operations research, such as manufacturing and logistics.
Potential for entirely new business models based on fully autonomous, optimized operational decision-making, leading to market consolidation or disruption.
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Read at arXiv cs.AI