arXiv Machine Learning July 30, 2026 By Elisabeth Fink

Learning the Word Problem: Geodesic Lengths and Cryptographic Applications

Read the original on arXiv Machine Learning →

arXiv:2607. 26241v1 Announce Type: cross Abstract: The Word Problem has been a subject of intensive mathematical study for over a century, initially driving advances in combinatorial group theory and more recently emerging as a foundational hardness assumption in post-quantum cryptography (PQC).

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

arXiv Machine Learning

Jun 26

Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy

arXiv:2606. 26873v1 Announce Type: cross Abstract: Graphs provide a natural language for relational data in chemistry, biology and optimisation.

By Snehal Raj, Brian Coyle, L\'eo Monbroussou, Andr\'e J. Ferreira-Martins, Renato M. S. Farias, Elham Kashefi

arXiv Machine Learning

Jun 9

Graph Neural Networks for Predicting Solvability of Finite Groups

arXiv:2606. 07619v1 Announce Type: new Abstract: We present a Graph Neural Network (GNN) framework for the classification of finite groups according to their solvability.

By Tal Weissblat

arXiv AI

Aug 11

Neurosymbolic Discovery of Algebraic Graph Constructions

arXiv:2608. 08118v1 Announce Type: new Abstract: There are several methods for searching for graphs with prescribed properties, such as SAT solvers and specialized generators.

By David Seka, Stefan Szeider

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jun 26

A General Framework for Learning Algebraic Properties from Cayley Graphs using Graph Neural Networks

arXiv:2606. 26212v1 Announce Type: new Abstract: A Graph Neural Network (GNN) framework for predicting the solvability of finite groups from their Cayley graph representations was introduced in [1].

By Tal Weissblat

arXiv Machine Learning

1d ago

Disentangling Homophily and Rarity: Explaining Failure in Graph Neural Networks

arXiv:2608. 14823v1 Announce Type: new Abstract: Are heterophilic nodes in a graph harder to classify because they are heterophilic or because they are rare?

By Preben M. Ness, Fariz Ikhwantri, Dusica Marijan

arXiv Machine Learning

Jul 15

Learning the Graphical Nature of Symmetries

arXiv:2607. 12026v1 Announce Type: cross Abstract: Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured, compared, and learned.

By Rashid Barket, Enrico Grimaldi, Yacoub Hendi, Edward Hirst, Adam Onus, Harmeet Singh

benchmarks

Related stories

Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy

Graph Neural Networks for Predicting Solvability of Finite Groups

Neurosymbolic Discovery of Algebraic Graph Constructions

A General Framework for Learning Algebraic Properties from Cayley Graphs using Graph Neural Networks

Disentangling Homophily and Rarity: Explaining Failure in Graph Neural Networks

Learning the Graphical Nature of Symmetries