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.
By Rafa{\l} Stachowiak, Tomasz P. Pawlak
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.
By Florentina Voboril, Stefan Szeider
The paper introduces a constraint‑aware training objective that externalizes program analysis constraints used during inference, avoiding redundant learning of these constraints during model training. It presents three theorems demonstrating that this approach can reduce model size and improve data efficiency. A controlled synthetic experiment confirms that constraint‑aware training achieves lower prediction loss than standard cross‑entropy training at the same parameter count and data volume.
By Jinwoo Kim
RECAST is a new framework that generates datasets with far more constraints per example than existing benchmarks, aiming to push large language models (LLMs) to better follow complex instructions. The authors built RECAST-30K, a 30,000‑instance dataset covering 19 constraint types extracted from real prompt‑response pairs, and showed that fine‑tuning on it improves LLMs’ ability to handle complex tasks without harming general performance. RECAST also provides rule‑based and LLM‑based validators for automatic constraint verification, enabling reward‑based reinforcement learning to further enhance model performance on challenging tasks.
By Zhengkang Guo, Wenhao Liu, Mingchen Xie, Jingwen Xu, Zisu Huang, Muzhao Tian, Jianhan Xu, Yuanzhe Shen, Qi Qian, Muling Wu, Xiaohua Wang, Changze Lv, He-Da Wang, Hu Yao, Xiaoqing Zheng, Xuanjing Huang
CATune is a Bayesian optimization framework that incorporates deterministic inter‑knob ordering constraints from DBMS documentation into the search space. By performing optimization within a constraint‑consistent subspace and using a topology‑aware sampling strategy, it avoids costly post‑hoc constraint handling. Experiments on PostgreSQL and MySQL with TPC‑C and TPC‑H workloads show that CATune achieves up to 12.5× faster convergence to the baseline optimum and improves throughput by up to 63.37%.
By Fangping Lan, Qi Zhang, Eduard Dragut
arXiv:2608. 15143v1 Announce Type: new Abstract: Constraint solving is a declarative approach for solving combinatorial satisfaction and optimization problems.
By Tias Guns, Ignace Bleukx, Hendrik Bierlee, Jo Devriendt, Emilio Gamba, Orestis Lomis, Wout Piessens, Thomas Sergeys, Dimos Tsouros, Wout Vanroose, H\'el\`ene Verhaeghe
arXiv:2606. 03624v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions, either by failing to satisfy individual constraints or by struggling to balance competing constraints simultaneously.
By Zhengyi Zhao, Shubo Zhang, Huimin Wang, Zezhong Wang, Yutian Zhao, Yefeng Zheng, Binyang Li, Yulan He, Kam-Fai Wong, Xian Wu
arXiv:2607. 04854v1 Announce Type: new Abstract: Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications.
By Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu
The paper introduces Primal-Dual Flow Matching (PDFM), a method for learning data generators that satisfy user‑specified sample‑wise constraints with a guaranteed probability of satisfaction. PDFM builds on a population‑level constrained negative log‑likelihood objective and a flow‑matching parameterization, providing theoretical guarantees under regularity and expressivity assumptions. Practically, PDFM requires only binary membership oracle feedback, avoids differentiable distance metrics or convexity assumptions, and empirically outperforms vanilla flow matching while maintaining distributional fidelity.
By Zhengyan Huan, Peter Y. Lu, Shuchin Aeron
The paper introduces a new automated feature extraction method for algorithm selection in constraint optimisation, using graph conversion and Weisfeiler‑Lehman (WL) graph kernels to produce structural representations of problem instances. It proposes a cut‑based WL feature (“WLc”) that captures structural partitions and claims this representation improves predictive performance over existing tools like “fzn2feat”. Experiments on MiniZinc Challenge instances for Borda‑count maximisation and predictive‑accuracy maximisation show that SVMs with WLc features outperform “fzn2feat”, while Random Forests and MLPs yield comparable results.
By Alessio Pellegrino, Jacopo Mauro
The paper introduces PolyFormer, a physics-informed machine learning framework that learns compact polytopic representations of complex constraints. By transforming constraint-induced geometry into efficient polytopic reformulations, PolyFormer reduces optimization complexity and enables the use of standard solvers. Evaluations on large‑scale resource aggregation, network‑constrained optimization, and uncertainty‑aware optimization show up to 6,400‑fold speedups and 99.87% memory savings while keeping feasibility and objective errors low.
By Yilin Wen, Yi Guo, Bo Zhao, Wei Qi, Zechun Hu, Colin Jones, Jian Sun
arXiv:2607. 22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive to solve.
By Anjian Li, Ryne Beeson