ORAgentBench: Can LLM Agents Solve Challenging Operations Research Tasks End to End?

• URL: http://arxiv.org/abs/2606.19787v1
• Date: Thu, 18 Jun 2026 04:43:23 GMT
• Title: ORAgentBench: Can LLM Agents Solve Challenging Operations Research Tasks End to End?
• Abstract summary: We introduce ORAgentBench, an execution-grounded benchmark for evaluating autonomous agents on operations research tasks.<n>It contains 107 human-reviewed tasks across diverse operational scenarios, each packaged with a natural-language brief, multi-file data, configuration artifacts, and a required submission schema.<n>Experiments with fourteen frontier agent-model configurations show that current agents remain far from reliable OR practice.
• Score: 28.383940617377856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 tasks. It contains 107 human-reviewed tasks across diverse operational scenarios, each packaged in an isolated environment with a natural-language brief, multi-file data, configuration artifacts, and a required submission schema. Agents must write and run solution code, and their submissions are evaluated by hidden validators for schema validity, hard-constraint feasibility, and normalized objective quality. Experiments with fourteen frontier agent-model configurations show that current agents remain far from reliable OR practice. The best agent passes only 35.51% of all tasks and 20.59% of hard tasks, and many feasible submissions still fall below the required quality threshold. Failure analysis further shows that errors are dominated by strategic weaknesses, including missed operational rules, brittle formulations, weak feasible-solution construction, and insufficient solution improvement. OR-specific procedural skills increase hard-task feasibility, but do not reliably improve solution quality or pass rate. These results suggest that progress in OR agents requires moving beyond plausible optimization code toward dependable, high-quality operational decision-making.

Related papers

• StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows [63.57092302641323]
  We introduce textbfStartupBench, an E2E agent benchmark grounded in market-validated AI startup products.<n>We study AI products with demonstrated adoption, together with their product and users, to identify real-world tasks for which AI has established practical demand.<n>Our results reveal that many market-validated agents remain beyond the reliable capabilities of current general-purpose agents.
  arXiv  Detail & Related papers  (2026-08-18T14:01:32Z)
• AgentAbstain: Do LLM Agents Know When Not to Act? [19.223843408018997]
  Existing evaluations mostly focus on task success rather than whether agents know when to abstain.<n>We present the first systematic evaluation framework for agentic abstention: the calibrated ability of tool-using LLM agents to recognize when not to act.<n>AgentAbstain is a paired-task benchmark built on an agent-native taxonomy of 8 abstention scenarios across pre-execution reasoning and runtime discovery.
  arXiv  Detail & Related papers  (2026-07-11T00:57:36Z)
• AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments [84.8170304874972]
  We present AgentGym2, a new evaluation framework with task instances grounded in real-world working demands.<n>It measures agents' ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information.
  arXiv  Detail & Related papers  (2026-07-06T14:56:51Z)
• Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents [23.69429098915734]
  Verifier-Guided Action Selection (VegAS) is a test-time framework designed to improve robustness of MLLM-based embodied agents.<n>We show that VeGAS consistently improves generalization, achieving up to a 36% relative performance gain over strong CoT baselines.
  arXiv  Detail & Related papers  (2026-05-12T18:08:24Z)
• OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation [57.505743202759646]
  OccuBench is a benchmark covering 100 real-world professional task scenarios across 10 industry categories and 65 specialized domains.<n>Our multi-agent synthesis pipeline automatically produces evaluation instances with guaranteed solvability, calibrated difficulty, and document-grounded diversity.
  arXiv  Detail & Related papers  (2026-04-13T00:27:32Z)
• From Task Solving to Robust Real-World Adaptation in LLM Agents [17.122224644097304]
  Large language models are increasingly deployed as specialized agents that plan, call tools, and take actions over extended horizons.<n>We benchmark agentic LLMs in a grid-based game with a simple goal but long-horizon execution.<n>We find large gaps between nominal task-solving and deployment-like robustness.
  arXiv  Detail & Related papers  (2026-02-02T20:10:40Z)
• AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios [49.90735676070039]
  The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow.<n>We argue that current evaluations prioritize increasing task difficulty without sufficiently addressing the diversity of agentic tasks.<n>We propose AgentIF-OneDay, aimed at determining whether general users can utilize natural language instructions and AI agents to complete a diverse array of daily tasks.
  arXiv  Detail & Related papers  (2026-01-28T13:49:18Z)
• AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress [71.02263260394261]
  Large language models (LLMs) still encounter challenges in multi-turn decision-making tasks.<n>We build process reward models (PRMs) to evaluate each decision and guide the agent's decision-making process.<n>AgentPRM captures both the interdependence between sequential decisions and their contribution to the final goal.
  arXiv  Detail & Related papers  (2025-11-11T14:57:54Z)
• OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks [52.87238755666243]
  We present OmniEAR, a framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks.<n>We model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains.<n>Our systematic evaluation reveals severe performance degradation when models must reason from constraints.
  arXiv  Detail & Related papers  (2025-08-07T17:54:15Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems.<n>In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy.<n> Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• Adaptive In-conversation Team Building for Language Model Agents [33.03550687362213]
  Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks.<n>Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent.<n>A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods.
  arXiv  Detail & Related papers  (2024-05-29T18:08:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.