From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks

2026-08-06

AI Summary

Existing physical AI systems struggle to maintain reliable world models under uncertainty, perform long-horizon planning, generalize across diverse scenarios, and support real-time collaboration due to architectural limitations. This work proposes the Holographic Digital Twin Networks (HDT-Nets) framework, which integrates holographic representations, active inference, causal Markov blankets, category theory, and integrated information theory to establish a semantic-preserving coordination mechanism among edge agents. HDT-Nets enables, for the first time, semantically consistent communication across heterogeneous agents, value-driven information exchange grounded in cognitive relevance, and quantifiable assessment of emergent collective intelligence. The framework substantially enhances real-time perception, decision-making, and learning capabilities in dynamic environments.

Abstract

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.

Problem

• Physical AI
• Digital Twins
• World Models
• Real-time Coordination
• Uncertainty

Innovation

• Holonic Digital Twins
• Active Inference
• Causal Markov Blanket
• Category Theory
• Integrated Information Theory