arXiv:2608. 11768v1 Announce Type: new Abstract: The adaptive neuro-fuzzy inference system (ANFIS) is an interpretable reasoning framework capable of generating explicit IF-THEN fuzzy rules, making it suitable for tasks requiring transparent reasoning.
By Haoran Pei, Zhao Su, Zetao Lin, Haoran Li, Jun Shen, Qi Zhu, Lan Guo, Qingguo Zhou, Binbin Yong
The paper introduces a multi-stage rule‑chaining framework for the Abstraction and Reasoning Corpus (ARC), aiming to model cognitive generalization by inferring abstract rules from few examples. It combines three solvers—a deterministic rule discovery module, a pattern‑composition engine, and a structural abstraction layer—executed sequentially in a fallback hierarchy that reuses earlier reasoning traces to improve interpretability and generalization. The system achieved over 95% accuracy on ARC tasks, demonstrating strong performance across deterministic, compositional, and abstract categories.
By Deblina Kar
arXiv:2512. 03578v3 Announce Type: replace-cross Abstract: Time series extrinsic regression (TSER) refers to the task of predicting a continuous target variable from an input time series.
By Florent Forest, Amaury Wei, Olga Fink
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)
Neural Structural Reasoner (NSR) is a brain-inspired neural network that preserves relational structure directly in the connectivity and dynamics of coupled neuronal populations. It incorporates multi-layered architecture, stable entity representations, and path integration to perform link prediction by parallelizing over candidate relational structures and using confidence-weighted scores. NSR achieves competitive accuracy on knowledge-graph benchmarks, trains faster than several neural baselines, and offers native interpretability by tracking human-readable neuron activations and extracting latent relational hierarchies and compositional rules.
By Zixing Jia, Yuhang Pan, Ni Ji
The paper introduces DiCEf, a variant of the DiCE counterfactual example generator that incorporates expert knowledge expressed as a fuzzy linguistic vocabulary. By embedding continuous data into this linguistic domain, DiCEf personalises counterfactual explanations to be linguistically perceptible while maintaining minimal cost, sparsity, and diversity. Experimental results on a real‑world dataset demonstrate that this approach yields semantically meaningful counterfactuals for the explainee.
By Akram Bensalem (IMT Atlantique - INFO), Fahima Djelil (Lab-STICC\_MOTEL, IMT Atlantique - INFO), Marie-Jeanne Lesot (IMT Atlantique - INFO, Lab-STICC, Lab-STICC\_MOTEL), Gr{\'e}gory Smits (IMT Atlantique - INFO, Lab-STICC, Lab-STICC\_MOTEL)