arXiv:2606. 31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users.
By Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek
arXiv:2607. 14315v1 Announce Type: cross Abstract: In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score.
By Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis, Dimitrios Kotios, Vasileios Koukos, Dimosthenis Kyriazis, Jonh Soldatos
arXiv:2607. 29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI).
By Christian Oliva, Luis F. Lago-Fern\'andez
arXiv:2606. 10942v1 Announce Type: cross Abstract: As artificial intelligence and machine learning (AI/ML) models become integral to network operations, their lack of transparency poses a significant barrier to operator trust.
By Kiarash Rezaei, Omran Ayoub, Sebastian Troia, Francesco Lelli, Paolo Monti, Carlos Natalino
The paper discusses how large language models retrieve facts from their weights, proposing that this phenomenon reflects a broader operation termed feature recall. It argues that a linear projection can be interpreted as retrieving stored information scaled by input activations, and demonstrates that feature recall applies across various architectures, contrasting it with the traditional feature combination paradigm. The authors also explore potential mechanistic identification of feature recall cases and suggest new empirical directions for mechanistic interpretability research.
By Pierre Beckmann
arXiv:2607. 13045v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a key paradigm for privacy-preserving collaborative model training across distributed and heterogeneous data sources.
By Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni