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 authors propose OptiAgent, a multi-agent framework that takes a natural language description of an Operations Research problem and produces a solver-ready mathematical formulation and executable code. The architecture emphasizes mathematical modeling with dedicated agents for structure extraction. A novel multi-loop validation architecture improves accuracy and transparency.

What the Document Says

• Proposed: OptiAgent, a multi-agent framework
• Takes natural language description of Operations Research problem
• Outputs solver-ready mathematical formulation and executable code
• Architecture prioritizes mathematical modeling
• Dedicated agents for structure extraction (decision variables, constraints)
• Introduced: novel multi-loop validation architecture
• Four specialized feedback mechanisms targeting distinct failure modes (misinterpretation, structural defects, mathematical inconsistencies, validation failures, and code errors)
• Achieves state-of-the-art performance on 3 out of 4 benchmarks for LP, MILP, and Nonlinear Programming tasks
• Remains highly competitive on the remaining dataset

Key Concepts Mentioned

• OptiAgent
• Operations Research problem
• mathematical formulation
• executable code
• multi-agent framework
• natural language description
• multi-loop validation architecture
• feedback mechanisms
• failure modes
• LP (Linear Programming)
• MILP (Mixed Integer Linear Programming)
• Nonlinear Programming

Claims Made by the Document

• OptiAgent produces solver-ready mathematical formulations and executable code for Operations Research problems given a natural language description.

Evidence Used

• The text mentions that OptiAgent takes a natural language description of an Operations Research problem and outputs solver-ready mathematical formulation and executable code.

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.