SAG‑Sep is a new separator for rounded capacity cuts (RCCs) in capacitated vehicle‑routing problems that uses a single encoder pass to score node membership across vehicle‑count levels, avoiding iterative predict‑and‑coarsen steps. It encodes the LP solution as a Sparse Augmented Graph (SAG) with flow‑informed multi‑hop edges, and leverages the nested, onion‑like structure of high‑value RCCs through a training‑free Onion‑Sweep decoder that ranks customers and emits the most violated prefixes. On the NeuralSEP benchmark with 1,000‑customer instances, SAG‑Sep with Onion‑Sweep cuts the average root gap by 17.7% over the best NeuralSEP baseline and achieves a roughly 30‑fold per‑iteration speedup.
By Haoran Liu, Guanyi Wang, Yu Yang
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 a new automated feature extraction method for algorithm selection in constraint optimisation, using graph conversion and Weisfeiler‑Lehman (WL) graph kernels to create structural representations of problem instances. It proposes a cut‑based WL feature (“WLc”) that captures structural partitions and provides a more detailed predictive signal. Experiments on MiniZinc Challenge instances show that WLc features outperform the traditional “fzn2feat” when used with Support Vector Machines, while results with Random Forests and Multi‑Layer Perceptrons are comparable.
arXiv:2608. 04398v1 Announce Type: cross Abstract: Robotic planning often involves multiple objectives with complex priority relationships, such as safety, efficiency, and regulatory compliance.
By Omar Muhammetkulyyev, Oren Salzman, Tichakorn Wongpiromsarn
arXiv:2608. 02612v1 Announce Type: cross Abstract: Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise.
By Yutaro Yamada, Kei Hiroshima, Nozomu Yoshinari, Kento Uchida, Shinichi Shirakawa
arXiv:2607. 28135v1 Announce Type: new Abstract: Machine learning for combinatorial optimization typically relies on neural constructors trained via reinforcement learning on large offline datasets for a fixed problem class-incurring high pretraining costs and generalizing poorly outside the training distribution.
By Mohand Mezmaz, Gr\'egoire Danoy
arXiv:2509. 24256v2 Announce Type: replace-cross Abstract: The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizable representations from vast datasets.
By Yunhao Liang, Pujun Zhang, Yuan Qu, Jingyuan Yang, Shaochong Lin, Zuo-jun Max Shen
GraphSkillEvo introduces a graph-structured representation for agent skills, where each node encodes an execution step and edges capture context-dependent transitions. This structure offers clearer workflow guidance and reduces redundancy compared to unstructured natural-language skills. The authors then present a population-based evolutionary optimization framework that explores this structured skill space, achieving higher accuracy than the baseline SkillOpt across five agent benchmarks.
By Rui Sun, Zhi Zheng, Zhenkun Wang, Zhichao Lu
The paper introduces GPSCS, a learning-based framework that uses genetic programming to evolve a symbolic scoring function for selecting cutting planes in mixed-integer linear programming. It also employs a statistics-based method to decide how many top-ranked cuts to add during each separation round. Experiments on diverse synthetic and real-world benchmarks show that GPSCS reduces solving time by 6% over SCIP’s default strategy and outperforms a neural-network approach and a no-cut baseline, indicating strong generalization and robustness.
By Gwen maudet, Mathis Da Cunha, Gr\'egoire Danoy
arXiv:2608. 13333v1 Announce Type: new Abstract: Large neighborhood search normally selects a random subset of decision variables for iterative optimization.
By Hai Xia, Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider
MultiEvoSkill is a skill‑optimization framework that transforms skill documents into typed graphs, enabling explicit procedural topology and parallel branches. It employs a heterogeneous agent system to generate revision suggestions based on diverse experiences, and a structure‑aware debate mechanism to resolve edits while respecting dependencies and importance constraints. Experiments show that this approach outperforms state‑of‑the‑art methods and improves cross‑model generalization of skill artifacts.
By Kounianhua Du, Jianxing Liu, Ruoshui Shan, Yucheng Zhang, Ke Li, Xing Sun, Weinan Zhang
arXiv:2606. 27161v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of visual tokens, which introduces substantial computational overhead.
By Tinghao Wang, Yichen Guo, Rui Huang, Zheng Lu, Qizhe Zhang, Chenxi Li, Yuan Zhang, Jiajun Cao, Zhirong Shen, Yaosong Du, Guangyan Gan, Wenya Wang, Lin William Cong, Shanghang Zhang