这篇arxiv论文把数字孪生从被动镜像变成主动智能体，用网络协同做物理AI推理，搞机器人或边缘智能的可以看看思路。
该论文提出HDT-Nets框架，用全息数字孪生网络实现实时物理AI推理。每个HDT采用从物理智能体到网络边缘的层次结构，本地自主推理并协作形成集体智能。框架利用因果马尔可夫毯协调感知、通信与控制，支持跨域反事实推理。主动推理通过最小化期望自由能统一感知、行动和学习，并依据认知价值决定传输哪些信念。范畴论保证异构智能体间语义结构保持，整合信息论量化集体智能何时超越独立运行。
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
Despite advances in artificial intelligence (AI) across multiple sectors, today's AI tools, including deep learning and generative AI, still fail when embedded into physical systems, such as robots and vehicles operating under real-world physical laws. This stems from their inability to maintain reliable world models for long-horizon planning under uncertainty and generalize to unseen scenarios. In this context, wireless networks, through pervasive sensing and communication, can orchestrate physical intelligence. However, current architectures optimize throughput, latency, and reliability and cannot support real-time physical AI coordination, requiring agents to maintain shared spatiotemporal context. To address these challenges, a network of holonic digital twins (HDT-Nets) framework is proposed to deliver real-time physical AI inference through holonic agents that actively reason about their environment rather than passively mirror physical assets. Each HDT is realized as a hierarchical structure spanning the physical agent and network edge, reasoning autonomously at the local level while cooperating with neighboring HDTs to form collectively intelligent units. In HDT-Net, causal Markov blankets spanning sensing, communication, and control determine which agents must coordinate and enable counterfactual reasoning over multi-domain interventions. Active inference within these boundaries unifies perception, action, and learning by minimizing expected free energy while deciding which beliefs to transmit based on their cognitive value to the receiver. Category theory ensures that transmitted beliefs preserve semantic structure across heterogeneous agents with incompatible representations. Finally, integrated information theory quantifies when collective intelligence exceeds independent operation and how network intelligence evolves through coordinated learning and information exchange.