Constraint acquisition needs better benchmarks
Constraint Acquisition (CA) and related research on Mathematical Programming (MP) model validation and enhancement are limited by inadequate benchmarks. Existing benchmarks are designed for solver evaluation, lacking domain knowledge artifacts. This work presents MPMMine, a benchmark suite guided by consistency, standardization, completeness, extensibility, openness, and version control. It uses open formats (MiniZinc, CommonMark, JSON) and provides multiple models per problem, tens of instances per model, and thousands of solutions and non-solutions in integer and continuous domains, along with natural-language descriptions.
Article intelligence
Key points
- CA research is hindered by insufficient benchmarks, affecting reproducibility and comparability.
- Existing benchmarks are solver-oriented and lack domain knowledge artifacts.
- MPMMine benchmark suite emphasizes consistency, standardization, open formats, and diverse data including natural-language descriptions.
- MPMMine provides multiple models, tens of instances per model, and thousands of solutions/non-solutions in integer and continuous domains.
Why it matters
This matters because CA research is hindered by insufficient benchmarks, affecting reproducibility and comparability.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26279] Constraint acquisition needs better benchmarks
[Submitted on 25 May 2026]
Title:Constraint acquisition needs better benchmarks
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Abstract:Constraint Acquisition (CA) and related research on the validation and enhancement of Mathematical Programming (MP) models from domain knowledge artifacts are currently limited by inadequate benchmarks. This deficiency impedes reproducibility and cross-study comparability, slowing the maturation of CA methods. Existing benchmarks were designed for solver evaluation rather than for assessing CA algorithms. They are loosely organized, treat individual problems inconsistently, and omit the domain knowledge artifacts required by CA methods. This work presents MPMMine, a benchmark suite designed to assess algorithms that discover, validate, and enhance MP models using diverse domain knowledge artifacts. MPMMine is guided by consistency, standardization, completeness, extensibility, openness, and version control. It adopts a uniform structure and relies on open formats: MiniZinc, CommonMark, and JSON. It provides multiple models per problem, tens of instances per model, and thousands of solutions and non-solutions in both integer and continuous domains, alongside natural-language descriptions to support text-to-model methods.
Comments: 12 pages, 1 figure, for the associated dataset, see this https URL
Subjects:
Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
MSC classes: 90C90 (Primary), 90C05 (Secondary)
ACM classes: I.6.3; I.2.2; I.2.7
Cite as: arXiv:2605.26279 [cs.AI]
(or arXiv:2605.26279v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.26279
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tomasz Pawlak [view email] [v1] Mon, 25 May 2026 19:05:12 UTC (801 KB)
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