arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.
By Agnese Chiatti, Michael Cochez, Cristina Cornelio, Sebastijan Dumancic, Artur d'Avila Garcez, Luis C. Lamb, Lia Morra, Mathias Niepert, Robert Peharz, Alberto Speranzon, Maarten Stol, Annette Ten Teije, Thiviyan Thanapalasingam, Frank Van Harmelen, Emile Van Krieken, Antonio Vergari, Benjie Wang
arXiv:2606. 03269v1 Announce Type: new Abstract: Visual Question Answering (VQA) is the task of answering questions about images, requiring the integration of multimodal input and reasoning.
By Thomas Eiter, Nelson Higuera Ruiz, Johannes Oetsch
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)
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. 13073v1 Announce Type: new Abstract: Neuro-symbolic AI based on $IFOL_B$ is a way to combine neural learning and symbolic reasoning to overcome limitations of purely neural systems (like lack of interpretability and logical structure) with formal logical machinery for self-reference.
By Zoran Majkic
arXiv:2611.07088v1 Announce Type: new
Abstract: Symbolic models offer a powerful account of cognition, but their scope has historically been limited by the need for hand-engineering and computational...
By Solim LeGris, Philipp Mondorf, Jinran Jin, Akshay K. Jagadish, Brenden M. Lake, Todd M. Gureckis