arXiv Machine Learning July 20, 2026 By Filip Sabo, Aida Todri-Sanial

Graph Coloring Approach to Solving Sudoku with Oscillatory Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2607. 15814v1 Announce Type: new Abstract: Oscillatory Neural Networks (ONNs) present an attractive physics-based computing paradigm rooted in the dynamics of a network of typically fully coupled oscillators aiming to minimize an underlying energy function.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

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Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration

arXiv:2608. 14569v1 Announce Type: new Abstract: Neural solvers for constraint satisfaction problems have achieved remarkable in-distribution accuracy, yet they suffer from a fundamental limitation persistent constraint violations occur under distribution shifts even when the model reports high confidence.

By Shufeng Kong, Xiaochuan Zhang, Caihua Liu

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arXiv AI

Jun 4

Learning Empirically Admissible Neural Heuristics for Combinatorial Search

arXiv:2606. 04860v1 Announce Type: cross Abstract: Finding optimal solution paths for combinatorial puzzles like the Rubik's Cube, sliding tile puzzles, and Lights Out remains a classical challenge in artificial intelligence.

By Siddharth Sahay

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arXiv AI

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DiBS: Diffusion-Informed Branch Selection

arXiv:2606. 06518v1 Announce Type: new Abstract: Sudoku is a representative constraint satisfaction problem that requires global structural reasoning under strict discrete constraints.

By Bo Liu, Yuan Xie, Yuan Gao, Xiaolong Luo, Peng Ye, Tao Chen, Fujun Han

diffusion benchmarks

arXiv Machine Learning

Jun 19

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System

arXiv:2606. 19754v1 Announce Type: new Abstract: Partial differential equations (PDEs) play a central role in modeling complex physical, biological, and engineering systems.

By Zhiwen Yu, Derong Yang, Liujian Zhang, Kaixiang Yang, Peilin Zhan, Jianmin Lv, Jane You, C. L. Philip Chen

arXiv Machine Learning

Jul 30

EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks

arXiv:2607. 26490v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance heavily relies on the manual, trial-and-error engineering of neural representations, loss formulations, and optimization dynamics.

By Peng Yin, Kai Li, Yifan Zhang, Jian Cheng

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arXiv Machine Learning

Jul 30

Physics-Informed Graph Neural Networks for Robust AC-Optimal Power Flow

arXiv:2410. 04818v2 Announce Type: replace-cross Abstract: We present PINCO, an unsupervised learning framework that integrates Graph Neural Networks with physics-informed neural networks for AC optimal power flow (AC-OPF) solutions.

By Anna Varbella, Damien Briens, Blazhe Gjorgiev, Giuseppe Alessio D'Inverno, Priya L. Donti, Giovanni Sansavini

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