OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Adriana Laurindo Monteiro, Nayse Fagundes, Gabriel Mattos Langeloh, Gustavo de Oliveira Kanno, Priscila Louise Aguirre, Thiago Costa Rizuti da Rocha, Victor Leme Beltran
• Venue: arXiv: cs.AI
• Published: 2026-07-06
• Collected: 2026-07-07
• URL: http://arxiv.org/abs/2607.05346v1
• PDF: https://arxiv.org/pdf/2607.05346v1
• DOI: UNKNOWN

Plain-Language Brief

The researchers have developed a multi-agent framework called OptiAgent that can take a natural language description of an Operations Research problem and produce a mathematically formulated solution along with executable code. The system prioritizes mathematical modeling, has a novel multi-loop validation architecture to correct errors, and improves transparency. It achieves state-of-the-art results on three out of four benchmarks for Linear Programming (LP), Mixed Integer Linear Programming (MILP), and Nonlinear Programming tasks.

What the Document Says

• The OptiAgent framework is proposed
• OptiAgent takes a natural language description of an Operations Research problem
• It produces a solver-ready mathematical formulation and executable code
• Architecture prioritizes the mathematical modeling step
• Dedicated agents extract structures like decision variables and constraints for iterative self-correction
• A novel multi-loop validation architecture is introduced with four feedback mechanisms targeting error types such as misinterpretation, structural defects, mathematical inconsistencies, validation failures, and code errors
• The system aims to improve transparency by exposing each agent's reasoning and feedback
• OptiAgent achieves state-of-the-art performance on 3 out of 4 benchmarks for LP, MILP, and Nonlinear Programming tasks
• It remains highly competitive on the remaining dataset

Key Concepts Mentioned

• OptiAgent
• Operations Research problem
• mathematical formulation
• executable code
• multi-agent framework
• natural language description
• mathematical modeling
• iterative self-correction
• validation architecture
• feedback mechanisms
• error types
• transparency

Claims Made by the Document

• OptiAgent can solve Operations Research problems using a natural language description
• The system achieves state-of-the-art results on certain benchmarks for LP, MILP, and Nonlinear Programming tasks

Evidence Used

• The text mentions the OptiAgent framework's ability to take a natural language description of an Operations Research problem and produce a mathematically formulated solution that achieves state-of-the-art performance on certain benchmarks.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-ready mathematical formulation as well as executable code. Our architecture prioritizes the mathematical modeling step, where dedicated agents extract structures, such as decision variables and constraints, enabling iterative self-correction. We introduce a novel multi-loop validation architecture with four specialized feedback mechanisms, each targeting a distinct failure mode such as misinterpretation, structural defects, mathematical inconsistencies, validation failures, and code errors. Alongside accuracy, our modular design improves the process of solving optimization problems by improving transparency, as each agent exposes its reasoning and feedback, making the full modeling process auditable. Our framework achieves state-of-the-art performance on 3 out of 4 benchmarks across LP, MILP, and Nonlinear Programming tasks, while remaining highly competitive on the remaining dataset.