TriVAL: A Tri-Validation Framework for Faithful Automatic Optimization Modeling
A tri-validation framework that performs explicit validation at three stages of automatic optimization modeling: semantic specification, mathematical formulation, and code generation, with a new benchmark NL4COP for combinatorial problems.
Article intelligence
Key points
- TriVAL performs explicit validation at three stages of automatic optimization modeling.
- The framework uses a construct-validate-revise loop to catch errors early and prevent accumulation.
- A new benchmark NL4COP with 150 instances across 50 problem types is introduced for challenging combinatorial problems.
- Experiments show TriVAL consistently outperforms state-of-the-art methods, especially on the hardest problems.
Why it matters
This matters because triVAL performs explicit validation at three stages of automatic optimization modeling.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.23966] TriVAL: A Tri-Validation Framework for Faithful Automatic Optimization Modeling
[Submitted on 12 May 2026]
Title:TriVAL: A Tri-Validation Framework for Faithful Automatic Optimization Modeling
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Abstract:Optimization modeling serves as the pivotal bridge between natural-language problem descriptions and optimization solvers, and remains a cornerstone for bringing operations research (OR) into real-world decision making. Recent advances in large language models (LLMs) have driven significant progress in automatic optimization modeling. However, existing methods still lack explicit validation during the modeling process, allowing errors introduced in earlier stages to carry through the pipeline and ultimately reduce final modeling accuracy. To address this challenge, we introduce TriVAL, a tri-validation framework that performs explicit validation at three stages of automatic optimization modeling: semantic specification, mathematical formulation, and code generation. At each stage, TriVAL follows a construct-validate-revise loop that assesses the current result against stage-specific criteria and revises it when needed. This design helps identify and correct errors before they accumulate across stages, helping preserve faithfulness throughout the modeling process. To evaluate automatic optimization modeling on more challenging combinatorial problems, we further introduce NL4COP, a benchmark of 150 instances across 50 diverse problem types with more complex decision logic, more tightly coupled constraints, and more demanding modeling requirements than existing benchmarks. Experiments on NL4COP and established benchmarks show that TriVAL consistently outperforms state-ofthe-art methods, with the largest gains on the most challenging problems.
Comments: 13 pages
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Combinatorics (math.CO)
MSC classes: 90C27, 68T20
Cite as: arXiv:2605.23966 [cs.CL]
(or arXiv:2605.23966v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.23966
arXiv-issued DOI via DataCite
Submission history
From: Jinghui Zhong [view email] [v1] Tue, 12 May 2026 23:13:20 UTC (5,686 KB)
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