Native Explainability for Bayesian Confidence Propagation Neural Networks: A Fra

arXiv:2603.18028v1 Announce Type: cross Abstract: While explainable AI (XAI) is often heralded as a means to enhance transparency and trustworthiness in closed-loop

arXiv:2605.11687v1 Announce Type: new Abstract: Financial institutions increasingly require AI explanations that are persistent, cross-validated across methods, and

arXiv:2604.27007v1 Announce Type: new Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and

arXiv:2605.21758v1 Announce Type: new Abstract: Explainable AI (XAI) methods identify which features are relevant to a model's predictions but often fail to clarify why certain

arXiv:2606.30953v1 Announce Type: new Abstract: We introduce the Neuro-Bayesian-Symbolic Residual Attention Shallow Network (NBS-RASN), a hybrid neural architecture for

arXiv:2604.15898v1 Announce Type: new Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models.

arXiv:2604.15371v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance across many natural language processing tasks, yet their decision

arXiv:2608.00492v1 Announce Type: new Abstract: Predictive coding offers a powerful theory of cortical computation, but corresponding scalable algorithmic implementations for