arXiv:2608. 14646v1 Announce Type: cross Abstract: Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reasoning.
By Xiaowei Jiang, Daniel Leong, Beining Cao, Nan Zhou, Yingtao Ren, Yu-Cheng Chang, Thomas Do, Chin-Teng Lin
arXiv:2605. 04193v2 Announce Type: replace Abstract: Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggle to scale to noisy and probabilistic settings.
By Iman Sharifi, Peng Wei, Saber Fallah
arXiv:2608. 16443v1 Announce Type: new Abstract: Neurosymbolic (NeSy) Artificial Intelligence aims to integrate Deep Learning (DL) architectures with symbolic reasoning.
By Riccardo Andreoni, Andrei Buliga, Alessandro Daniele, Paolo Felli, Chiara Ghidini, Marco Montali, Massimiliano Ronzani
arXiv:2607. 01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision.
By Pavel Iakovets, Liyanapathiranage Sudeepika Wajirakumari Samarathunga, Martin Thomas Horsch, Fadi Al Machot
arXiv:2605. 01797v2 Announce Type: replace Abstract: Integration of Answer Set Programming (ASP) with neural networks has emerged as a promising tool in Neuro-symbolic AI.
By Thomas Eiter, Katsumi Inoue, Sota Moriyama
arXiv:2607. 21185v1 Announce Type: new Abstract: Neurosymbolic (NeSy) systems integrate neural networks with logical reasoning to achieve both generalization and interpretability, but recent work has shown they are susceptible to shortcut reasoning behaviors.
By Akihiro Takemura (National Institute of Informatics, Tokyo, Japan), Katsumi Inoue (National Institute of Informatics, Tokyo, Japan)