MPMMine introduces a comprehensive benchmark suite for Constraint Acquisition (CA) in Mathematical Programming, addressing shortcomings of existing datasets such as inconsistent formats and limited problem classes. The suite offers standardized, extensible artifacts—including multiple model formulations, numerous parameter instantiations, and thousands of solutions and non‑solutions across discrete, continuous, and mixed domains—alongside natural‑language descriptions for text‑to‑model methods. Accompanying tools, the mpmmine‑py API and an interactive leaderboard, enable consistent evaluation of CA algorithms within isolated containers.
arXiv:2608. 08127v1 Announce Type: new Abstract: The runtime of Constraint Programming (CP) solvers is highly sensitive to modeling choices, such as symmetry breaking, implied constraints, global constraints, constraint reformulation, and variable representation.
GradSAT is a new framework that improves satisfiability solving for Quantifier-Free Floating-Point formulas by treating each SMT clause as an independent multi‑task learning problem. It uses dynamic gradient normalization (GradNorm) to balance gradient magnitudes across clauses, preventing a few difficult clauses from dominating the optimization trajectory. The approach combines a GPU‑accelerated PyTorch backend for continuous relaxation with a precise local search engine, yielding a robust, highly parallelizable solver.
arXiv:2608. 15143v1 Announce Type: new Abstract: Constraint solving is a declarative approach for solving combinatorial satisfaction and optimization problems.
CCTU is a new benchmark designed to evaluate large language models (LLMs) on their ability to use tools under complex constraints. It includes 200 test cases that average seven constraint types and 4,700‑token prompts, covering resource, behavior, toolset, and response dimensions. An executable validation module performs step‑level checks, and nine state‑of‑the‑art LLMs were tested, revealing that none exceed a 20% task completion rate when strict constraints are enforced, with frequent violations and limited self‑refinement.
arXiv:2608. 02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workflows increase inference cost.
arXiv:2605. 19276v3 Announce Type: replace-cross Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs).
arXiv:2607. 14818v1 Announce Type: cross Abstract: We report on CoreForge, an experience in using large language models (LLMs) to build an unweighted MaxSAT solver from research papers rather than from an existing solver codebase.
The paper presents a Constraint Acquisition Tool that creates size‑independent constraint programs from input data and both positive and negative example solutions across multiple problem instances. It first derives size‑specific models using common patterns in global and binary constraints, then generalizes these into a model that can be applied to unseen data. The tool outputs MiniZinc code and a textual model description, and its effectiveness is demonstrated on the PTHG21 Challenge problems.
arXiv:2605.27981v2 Announce Type: replace Abstract: Evaluating the efficiency of algorithmic code requires test cases that expose runtime bottlenecks. Previous methods generate efficiency test cases...
arXiv:2607. 18256v1 Announce Type: new Abstract: Optimization modeling is the process of translating real-world decision problems, often described in natural language, into formal mathematical formulations and executable solver code.
arXiv:2606. 04816v1 Announce Type: new Abstract: Large language models (LLMs) increasingly translate natural-language optimization problems into executable solver code.
The paper introduces OptTips, a knowledge base of 50 expert optimization modeling techniques, and OptDachshund, a multi‑agent framework that generates mathematical models and solver code from natural‑language problem descriptions. Using these tools, the authors create the EfficientOpt benchmark, comprising 561 expert‑reviewed tasks with paired reference implementations, to evaluate large language models (LLMs) on both correctness and computational efficiency. Their experiments with 11 LLMs show a consistent efficiency gap: even when LLMs produce correct solutions, the resulting programs often take longer to solve than expert‑crafted counterparts, highlighting the need to assess both accuracy and runtime performance in LLM‑based optimization modeling.