发表机构
Worcester Polytechnic Institute; Virginia Tech（伍斯特理工学院; 弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对当前AI嵌入物理系统失效、无线网络架构无法支持实时物理AI协调的问题，提出HDT-Nets框架，通过holonic数字孪生实现实时物理AI推理，为网络物理AI提供新方案。
AI 中文摘要
尽管人工智能（AI）在多个领域取得了进展，但如今的AI工具，包括深度学习和生成式AI，在嵌入机器人、车辆等受现实物理定律约束的物理系统时仍然失效。这源于它们无法在不确定性下维持用于长程规划的可靠世界模型，也无法泛化到未见场景。在此背景下，无线网络可通过泛在感知与通信来协调物理智能，但当前架构仅优化吞吐量、延迟和可靠性，无法支持智能体维持共享时空上下文所需的实时物理AI协调。为应对这些挑战，本文提出 holonic 数字孪生网络（HDT-Nets）框架，通过主动推理环境而非被动镜像物理资产的 holonic 智能体，实现实时物理AI推理。每个HDT实现为跨越物理智能体与网络边缘的分层结构，在本地自主推理的同时，与相邻HDT协作形成集体智能单元。在HDT-Net中，跨越感知、通信与控制的因果马尔可夫毯决定了哪些智能体必须协调，并支持多域干预下的反事实推理。这些边界内的主动推理通过最小化预期自由能，统一感知、行动与学习，同时基于信念对接收方的认知价值决定传输哪些信念。范畴论确保传输的信念在具有不兼容表示的异构智能体间保留语义结构。最后，整合信息论量化集体智能何时超过独立运行，以及网络智能如何通过协作学习与信息交换演化。
英文摘要
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.
