“X of Information” Continuum: A Survey on AI-Driven Multi-Dimensional Metrics for Next-Generation Networked Systems

The development of next-generation networking systems has shifted from throughput-based paradigms towards intelligent, information-aware designs that emphasize information quality, relevance, and utility rather than data volume. Classical network metrics such as latency and packet loss remain significant but are insufficient for modern intelligent applications that include autonomous vehicles, digital twins, and metaverse environments. This survey presents the first comprehensive study of the “X of Information” continuum through a systematic four-dimensional taxonomic framework that structures information metrics along temporal, quality/utility, reliability/robustness, and network/communication dimensions. We uncover increasing interdependencies among these dimensions, where temporal freshness triggers quality evaluation, which enables reliability appraisal that supports effective network delivery. Our analysis reveals that artificial intelligence technologies such as deep reinforcement learning, multi-agent systems, and neural optimization models enable adaptive, context-aware optimization of competing information quality objectives. Our study of six critical application domains that span autonomous transportation, industrial IoT, healthcare digital twins, UAV communications, LLM ecosystems, and metaverse settings illustrates the promise of multi-dimensional information metrics for diverse operational needs. The survey identifies key implementation challenges that include metric integration, information-driven resource allocation, semantic communication, and federated learning optimization. We highlight critical research agendas in unified theoretical models, AI-augmented dynamic optimization, and cross-layer orchestration mechanisms that lay the foundation for intelligent, value-aware communication systems.