Fusing digital twins (DTs) with world models (WMs) promises a leap from reactive monitoring to proactive control. However, deploying high-fidelity WMs faces a fundamental cognitive gap: the immense computational demand of foundation models conflicts with the resource constraints of 6G edge nodes, while privacy regulations hinder the centralization of raw sensory data required for training. To bridge this gap, this paper presents HongAvg, a hierarchical on-demand cognitive split federated learning framework designed as cognitive infrastructure for next-generation DTs. By establishing a national-basin-edge three-tier architecture, HongAvg introduces foundation models to resource-constrained edges via split computing, offloading heavy cognitive reasoning while preserving data privacy. We propose a dual-stream semantic consistency mechanism to align edge interactions with the foundation model's cognition, ensuring that distributed cognitive primitives serve as valid inputs for global state estimation. Validated on a heterogeneous benchmark, HongAvg serves as a prototype for industrial cognitive computing, improving accuracy in visual monitoring by up to 8.9% and reducing edge memory usage by approximately 75%. This work provides the scalable architectural prerequisite necessary for evolving static DTs into proactive WM-driven multi-agent orchestrated intelligent systems.
Yue Wang, Jixuan Xie, Yusheng Lin et al.· IEEE Transactions on Network...· 0 citations
Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and interactive behavior. We therefore introduce MatrAIx, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users. MatrAIx has three core components: First, Persona 8B contains 8.3 billion persona records represented by 1,290 categorical dimensions. Records are either sampled from a dependency graph that preserves correlated attributes or derived from human-authored profiles. We release a quality-filtered coreset of approximately 1 million personas, comprising 599,847 human-grounded and 400,000 synthetic records. Second, the MatrAIx Playground provides four environments in which diverse users evaluate and interact with digital products: Survey, AI Chatbot, Web, and App. Third, MatrAIx provides 1,010 application tasks spanning more than 25 domains, including Commerce, Software, Finance, and Healthcare. We conducted 18,189 evaluation trials across eight representative tasks. Persona agents were powered by three LLMs: Claude Opus 4.8, GPT 5.5, and Claude Haiku 4.5. The resulting feedback captures how decisions and preferences vary across persona backgrounds, including hesitation after a price increase, willingness to continue after an AI assistant fails, and latency tolerance. We conducted two main validation studies: First, a 400-trial controlled study evaluated persona adherence across ten behavioral attributes and all four environments. The declared behavior was expressed or correctly suppressed in 366 trials (91.5%). Second, human and LLM judges evaluated the extraction quality of human-grounded personas. Overall, MatrAIx provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.
Xiaomin Li, Yuexing Hao, Jian Hou et al.· 0 citations
