A Lifelong Neuro-Symbolic Explainable AI Twin for Zero-Touch 6G IoE Management Under Non-Stationary Network Dynamics

Abstract

Future sixth-generation (6G) Internet of Everything (IoE) networks require zero-touch management systems capable of autonomously allocating communication and computational resources, satisfying service-level constraints, and providing transparent explanations for their decisions. Existing neuro-symbolic explainable artificial intelligence (XAI) twins improve interpretability by coupling neural prediction with symbolic reasoning; however, most rely on statically engineered causal graphs, fixed rule sets, and immutable knowledge bases. Such closed-world assumptions are unsuitable for non-stationary 6G environments in which radio conditions, mobility patterns, interference relationships, and service requirements continuously evolve. To address this limitation, this paper proposes LL-NS-XAI, a Lifelong Learning Neuro-Symbolic XAI Digital Twin for zero-touch 6G IoE management. The framework integrates three tightly coupled planes: an online implicit learner at the physical edge for time-varying service association and uplink/downlink resource allocation; a dynamic explicit learner in the virtual core for directed acyclic graph (DAG)-based Bayesian reasoning, most-probable-explanation inference, and explainable trust scoring; and a meta-cognitive layer that monitors reasoning residuals, detects parameter drift, structural drift, and ontological emergence, and autonomously revises the causal graph, probabilistic rule sets, conditional probability tables, and knowledge base. Unlike static neuro-symbolic twins, LL-NS-XAI treats realized post-decision outcomes, including achieved channel quality indicator (CQI), measured end-to-end delay, and throughput, as interventional evidence for online causal-model adaptation. Experimental evaluation using a non-stationary 5G-derived dataset with injected parameter drift, structural drift, and emergent IoE service classes demonstrates that LL-NS-XAI maintains higher long-term trustworthiness, lower reasoning residuals, and bounded performance degradation compared with static XAI twins, non-stationary bandits, and pure continual-learning baselines. The framework additionally accommodates a previously unseen IoE service class through autonomous causal-graph expansion, recovering near-oracle performance without offline retraining. These results demonstrate that lifelong causal adaptation can improve the robustness, explainability, and autonomy of AI-enabled zero-touch management in evolving 6G IoE networks.