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