Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks

2026-07-13 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Internet of Things (IoT) & Connected Devices · Depth: Expert, extended

Summary

The paper introduces LDT-Coord, a networked coordination framework for heterogeneous large language model (LLM) embodied agent teams deployed in physical AI environments like smart factories and service robotics. LDT-Coord utilizes a lightweight digital twin (DT) server where each agent independently selects actions and reports structured temporal constraints over shared resources. This approach decouples coordination performance from natural-language reasoning ability. The DT then executes a training-free, rule-based orchestrator algorithm to resolve cross-agent conflicts. To further minimize communication overhead, agent reporting control is formulated as a constrained partially observable Markov decision process (C-POMDP) and solved with PPO-Lagrangian. Simulation results demonstrate that LDT-Coord achieves a task success rate comparable to conventional methods while reducing communication overhead by over 70x and maintaining robustness under LLM heterogeneity.

Key takeaway

For AI Scientists and Robotics Engineers designing multi-agent systems, LDT-Coord offers a robust, low-latency solution to overcome the communication and heterogeneity challenges of LLM-driven embodied agents. You should consider adopting its structured communication primitives and training-free, rule-based orchestration to efficiently scale heterogeneous LLM teams in real-time physical AI applications, significantly reducing communication overhead compared to natural-language dialogue methods.

Key insights

LDT-Coord enables efficient, robust coordination for heterogeneous LLM agents via structured DT communication and rule-based conflict resolution.

Principles

• Decouple coordination quality from natural-language reasoning.
• Structured communication reduces overhead and improves robustness.
• Rule-based orchestrators can resolve atomic task conflicts.

Method

Agents select actions and report structured temporal constraints to a DT. The DT uses a training-free, rule-based orchestrator to resolve conflicts. Communication selection is optimized via C-POMDP and PPO-Lagrangian.

In practice

• Deploy in smart factories and service robotics.
• Reduce communication overhead in multi-agent systems.
• Coordinate heterogeneous LLM teams without performance degradation.

Topics

• Digital Twin
• LLM Embodied Agents
• Multi-Agent Coordination
• Communication Efficiency
• Reinforcement Learning
• Robotics
• Computing Power Networks

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.