Recognition: 1 theorem link
ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling
Pith reviewed 2026-05-08 18:51 UTC · model grok-4.3
The pith
Agentic LLM system converts vague business problems to optimization models
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
ORPilot introduces four components—an interview agent to draw out complete specifications from ambiguous inputs, a data collection agent that fetches operational data independently, a parameter computation agent that turns raw tables into model-ready values, and a solver-agnostic intermediate representation that recompiles deterministically to Gurobi, CPLEX, PuLP, Pyomo, or OR-Tools—plus retry loops driven by solver tracebacks. This architecture targets production conditions of vague problem statements and large-scale raw data rather than preformatted academic examples, and evaluation on real problems plus the IndustryOR, NL4OPT, and NLP4LP benchmarks shows higher accuracy on IndustryOR and,
What carries the argument
The solver-agnostic Intermediate Representation paired with the four specialized agents that separately manage specification elicitation, independent data retrieval, parameter derivation from raw tables, and model assembly.
If this is right
- Models generated once can be moved to any supported solver without rewriting code.
- Large raw data sets can be turned into parameters without separate preprocessing steps.
- Ambiguous business descriptions can be completed through automated back-and-forth interviews.
- Solver feedback can drive targeted automatic repairs instead of full restarts.
Where Pith is reading between the lines
- The same agent-plus-IR pattern could be tested on dynamic problems where data updates arrive over time.
- Direct links to company data warehouses would reduce the need for the data collection agent to search externally.
- Similar structures might apply to other modeling tasks such as simulation setup or constraint-based planning.
Load-bearing premise
The four agents can reliably draw out full specifications, locate and reshape large raw data sets, and generate correct models from unclear starting descriptions with only the built-in retry loops.
What would settle it
Apply ORPilot to a fresh collection of production problems that contain ambiguous text and voluminous raw operational tables, then measure whether the output models solve correctly after retries at a rate clearly below human-expert models or prior tools.
Figures
read the original abstract
This paper presents ORPilot, an open-source agentic AI system that translates real-world business problems into solver-ready optimization models. Unlike academic LLM-for-OR tools that assume clean problem specifications with preformatted inline data, ORPilot is designed for production conditions: ambiguous descriptions, large-scale raw operational data, and the need for portability across solver backends. The system introduces four novel components: (1) a conversational interview agent to elicit complete problem specifications, (2) a data collection agent that retrieves data independently of prompts, (3) a parameter computation agent to bridge raw tabular data and model-ready parameters, and (4) a solver-agnostic Intermediate Representation (IR) for deterministic, zero-LLM-call recompilation to Gurobi, CPLEX, PuLP, Pyomo, or OR-Tools solvers. Additionally, self-correcting retry loops utilize solver tracebacks for targeted repairs. ORPilot represents the first attempt to target production-level business problems rather than textbook operations research (OR) cases. Evaluation on real-world problems demonstrates promising results. When tested against traditional academic benchmarks: IndustryOR, NL4OPT and NLP4LP, ORPilot outperformed state-of-the-art tools in accuracy on the IndustryOR benchmark and delivered comparable performance on NL4OPT and NLP4LP.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript introduces ORPilot, an open-source agentic LLM system for translating real-world business optimization problems—characterized by ambiguous descriptions and large-scale raw operational data—into solver-ready models. It proposes four specialized agents (conversational interview for specification elicitation, independent data collection, parameter computation from raw tables, and solver-agnostic Intermediate Representation generation) plus self-correcting retry loops that use solver tracebacks. The authors position it as the first production-oriented tool rather than an academic benchmark solver, claiming promising results on real-world problems and outperformance versus state-of-the-art tools on the IndustryOR benchmark with comparable results on NL4OPT and NLP4LP.
Significance. If the empirical claims are substantiated with quantitative evidence, the work could meaningfully advance practical LLM-assisted optimization by addressing the gap between clean academic benchmarks and messy production settings. The solver-agnostic IR and open-source release are concrete strengths that would enable reproducibility and broader adoption if the agent reliability under ambiguity and scale is demonstrated.
major comments (3)
- [Abstract and Evaluation] Abstract and Evaluation section: the central claim of outperformance on IndustryOR (and the production-oriented positioning) is asserted without any reported accuracy percentages, number of instances, statistical significance tests, error breakdowns, or baseline details, leaving the primary result without visible supporting evidence.
- [Section 3] Section 3 (agent descriptions): the reliability of the four agents for eliciting complete specifications, retrieving/transforming large raw data, and generating correct models from ambiguous inputs is load-bearing for the production claim, yet no quantitative metrics (success rates, human intervention counts, failure modes after retry loops) are provided for real-world cases.
- [Evaluation] Evaluation section: the academic benchmarks (IndustryOR, NL4OPT, NLP4LP) are described as using clean preformatted inputs; the manuscript does not explain how (or whether) they were modified to test the stated production conditions of ambiguity and large-scale raw data, weakening the link between reported results and the core contribution.
minor comments (2)
- [Section 3.4] Notation for the Intermediate Representation (IR) should be formalized with a small example showing the deterministic recompilation step to at least two solvers.
- [Section 3] The manuscript would benefit from a table summarizing the four agents, their inputs/outputs, and LLM calls per stage for clarity.
Simulated Author's Rebuttal
Thank you for your thorough review and valuable feedback on our manuscript describing ORPilot. We have carefully considered each of the major comments and provide detailed responses below, along with indications of planned revisions to address the concerns raised.
read point-by-point responses
-
Referee: [Abstract and Evaluation] Abstract and Evaluation section: the central claim of outperformance on IndustryOR (and the production-oriented positioning) is asserted without any reported accuracy percentages, number of instances, statistical significance tests, error breakdowns, or baseline details, leaving the primary result without visible supporting evidence.
Authors: We agree that providing more quantitative details would strengthen the presentation of our results. The Evaluation section of the manuscript includes comparisons on the IndustryOR benchmark, but to make the evidence more visible, we will revise both the abstract and the Evaluation section to report specific accuracy percentages, the number of problem instances used, details on the baselines compared against, and any statistical significance tests performed. Error breakdowns by category will also be added where relevant. This revision will substantiate the outperformance claim with concrete data. revision: yes
-
Referee: [Section 3] Section 3 (agent descriptions): the reliability of the four agents for eliciting complete specifications, retrieving/transforming large raw data, and generating correct models from ambiguous inputs is load-bearing for the production claim, yet no quantitative metrics (success rates, human intervention counts, failure modes after retry loops) are provided for real-world cases.
Authors: The agent designs are intended to address the challenges of ambiguous and large-scale inputs in production settings. However, we recognize that quantitative metrics on their performance would better support the production-oriented claims. In the revised manuscript, we will include quantitative metrics for the agents based on our real-world case studies, such as success rates in specification elicitation and data handling, the average number of human interventions, and a breakdown of failure modes encountered and resolved via the retry loops. revision: yes
-
Referee: [Evaluation] Evaluation section: the academic benchmarks (IndustryOR, NL4OPT, NLP4LP) are described as using clean preformatted inputs; the manuscript does not explain how (or whether) they were modified to test the stated production conditions of ambiguity and large-scale raw data, weakening the link between reported results and the core contribution.
Authors: The academic benchmarks were evaluated in their standard, clean formats to allow fair comparison with existing state-of-the-art methods on established tasks. Our manuscript distinguishes these from the real-world problems used to demonstrate handling of ambiguity and raw data. We will revise the Evaluation section to explicitly clarify that no modifications were made to the benchmarks for production conditions, as the production aspects are validated separately through our real-world evaluations. This will strengthen the connection between the reported results and the core contributions by highlighting the complementary nature of the two evaluation types. revision: partial
Circularity Check
No circularity: system-description paper with no derivations or predictions
full rationale
The paper describes an agentic LLM system (four agents plus solver-agnostic IR) for translating business problems into optimization models. It contains no mathematical derivations, equations, fitted parameters, or 'predictions' that could reduce to inputs by construction. Claims rest on direct system design and empirical benchmark results (IndustryOR, NL4OPT, NLP4LP) rather than self-referential definitions, self-citation chains, or ansatzes. The evaluation is external and falsifiable, making the work self-contained with no load-bearing circular steps.
Axiom & Free-Parameter Ledger
Reference graph
Works this paper leans on
-
[1]
Optimus: Optimization modeling using MIP solvers and large language models
A. AhmadiTeshnizi, W. Gao, and M. Udell, “OptiMUS: Optimization modeling using MIP solvers and large language models,”arXiv:2310.06116v2 [cs.AI], pp. 1–19, 2023
-
[2]
arXiv preprint arXiv:2402.10172 , year =
——, “OptiMUS: Scalable optimization modeling with (MI)LP solvers and large language models,” arXiv:2402.10172v1 [cs.AI], pp. 1–17, 2024
-
[3]
arXiv preprint arXiv:2407.19633 , year =
A. AhmadiTeshnizi, W. Gao, H. Brunborg, S. Talaei, C. Lawless, and M. Udell, “OptiMUS-0.3: Using large language models to model and solve optimization problems at scale,”arXiv:2407.19633v3 [cs.AI], pp. 1–44, 2025
-
[4]
Orlm: Training large language models for optimization modeling,
C. Huang, Z. Tang, S. Hu, R. Jiang, X. Zheng, D. Ge, B. Wang, and Z. Wang, “ORLM: A customizable framework in training large models for automated optimization modeling,”arXiv:2405.17743v5 [cs.CL], pp. 1–12, 2025
-
[5]
Nl4opt competition: Formulating optimization problems based on their natural language descriptions
R. Ramamonjison, T. T. Yu, R. Li, H. Li, G. Carenini, B. Ghaddar, S. He, M. Mostajabdaveh, A. Banitalebi- Dehkordi, Z. Zhou, and Y. Zhang, “NL4Opt competition: Formulating optimization problems based on their natural language descriptions,”arXiv:2303.08233v2 [cs.CL], pp. 1–15, 2023
-
[6]
I’m a trader planning my next move. I would like to maxi- mize my profit. Today is{date}
Y. Wang and K. Li, “Large language models in operations research: Methods, applications, and challenges,” arXiv:2509.18180v3 [cs.AI], pp. 1–16, 2025
-
[7]
Chain- of-experts: When LLMs meet complex operations research problems,
Z. Xiao, D. Zhang, Y. Wu, L. Xu, Y. Wang, X. Han, X. Fu, T. Zhong, J. Zeng, M. Song, and G. Chen, “Chain- of-experts: When LLMs meet complex operations research problems,”ICLR 2024, pp. 1–19, 2024
2024
-
[8]
arXiv preprint arXiv:2503.10009 , year=
B. Zhang, P. Luo, G. Yang, B.-H. Soong, and C. Yuen, “OR-LLM-Agent: Automating modeling and solving of operations research optimization problems with reasoning LLM,”arXiv:2503.10009v3 [cs.AI], pp. 1–8, 2025
-
[9]
Autoformulation of mathematical optimization models using llms.arXiv preprint arXiv:2411.01679, 2024
N. Astorga, T. Liu, Y. Xiao, and M. van der Schaar, “Autoformulation of mathematical optimization models using LLMs,”arXiv:2411.01679v2 [cs.LG], pp. 1–23, 2025
-
[10]
C. Jiang, X. Shu, H. Qian, X. Lu, J. Zhou, A. Zhou, and Y. Yu, “LLMOPT: Learning to define and solve general optimization problems from scratch,”arXiv:2410.13213v2 [cs.AI], pp. 1–27, 2025
-
[11]
arXiv preprint arXiv:2403.01131 , year=
Z. Ma, H. Guo, J. Chen, Z. C. Guojun Peng, Y. Ma, and Y.-J. Gong, “LLaMoCo: Instruction tuning of large language models for optimization code generation,”arXiv:2403.01131v2 [math.OC], pp. 1–21, 2024
-
[12]
H. Lu, Z. Xie, Y. Wu, C. Ren, Y. Chen, and Z. Wen, “OptMATH: A scalable bidirectional data synthesis framework for optimization modeling,”arXiv:2502.11102v2 [cs.AI], pp. 1–36, 2025
-
[13]
NL2OR: Solve complex operations research problems using natural language inputs,
J. Li, R. Wickman, S. Bhatnagar, R. K. Maity, and A. Mukherjee, “NL2OR: Solve complex operations research problems using natural language inputs,”arXiv:2408.07272v1 [cs.AI], pp. 1–17, 2024
-
[14]
EquivaMap: Leveraging LLMs for automatic equivalence checking of optimization formulations,
H. Zhai, C. Lawless, E. Vitercik, and L. Leqi, “EquivaMap: Leveraging LLMs for automatic equivalence checking of optimization formulations,”arXiv:2502.14760v2 [cs.AI], pp. 1–20, 2025
-
[15]
arXiv preprint arXiv:2307.03875 , year=
B. Li, K. Mellou, B. Zhang, J. Pathuri, and I. Menache, “Large language models for supply chain optimization,” arXiv:2307.03875v2 [cs.AI], pp. 1–30, 2023
-
[16]
Solving general natural-language-description optimization problems with large language models,
J. Zhang, W. Wang, S. Guo, L. Wang, F. Lin, C. Yang, and W. Yin, “Solving general natural-language-description optimization problems with large language models,”arXiv:2407.07924v1, pp. 1–8, 2024
-
[17]
From large language models and optimization to decision optimization copilot: A research manifesto,
S. Wasserkrug, L. Boussioux, D. den Hertog, F. Mirzazadeh, I. Birbil, J. Kurtz, and D. Maragno, “From large language models and optimization to decision optimization copilot: A research manifesto,”arXiv:2402.16269v1 [cs.AI], pp. 1–27, 2024. 16
-
[18]
M. Peng, Z. Chen, J. Yang, J. Huang, Z. Shi, Q. Liu, X. Li, and L. Gao, “Automatic MILP model construction for multi-robot task allocation and scheduling based on large language models,”arXiv:2503.13813v1 [cs.AI], pp. 1–7, 2025
-
[19]
Langgraph,
LangChain, “Langgraph,” https://github.com/langchain-ai/langgraph, 2024, accessed: 2026-03-03. 17 Appendices Appendix A Conversation History between Interview Agent and User for Problem in Section 4.1 Agent: What is the main goal you want to achieve with this optimization model? For example, are you trying to minimize costs, maximize profit, or optimize s...
2024
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.