Generalization Bounds of Emergent Communications for Agentic AI Networking

2026-05-08

AI Summary

Existing emergent communication approaches often neglect physical network constraints and lack a solid information-theoretic foundation, hindering their ability to support task-oriented multi-agent collaboration. This work proposes a joint optimization framework grounded in multi-agent, multi-task distributed information bottleneck theory, introducing this principle into emergent communication for the first time. The framework establishes an end-to-end learning mechanism that jointly accounts for task relevance, bandwidth limitations, and computational complexity, while also deriving theoretical generalization bounds for communication protocols under decentralized inference. Experimental evaluation on a real hardware prototype demonstrates that the proposed method significantly outperforms existing approaches and exhibits superior generalization performance in unseen environmental conditions.

Abstract

The evolution of 6G networking toward agentic AI networking (AgentNet) systems requires a shift from traditional data pipelines to task-aware, agentic AI-native communication solutions. Emergent communication, a novel communication paradigm in which autonomous agents learn their own signaling protocols through interaction, is increasingly viewed as a promising solution to address the challenges posed by existing rigid, predefined protocol-based networking architecture. However, most existing emergent communication frameworks fail to account for physical networking constraints, such as bandwidth and computational complexity, and often lack a rigorous information-theoretical foundation. To address these challenges, this paper introduces a novel emergent communication framework that facilitates collaborative task-solving among heterogeneous agents through an information-theoretic lens. We propose a novel joint loss function that unifies the optimization of decision-making functions and the learning of communication signaling. Our proposed solution is grounded on the multi-agent and multi-task distributed information bottleneck (DIB) theory, which allows the quantification of the fundamental trade-off between task-relevant information representation and computational complexity. We further provide theoretical generalization bounds of the emergent communication protocol during decentralized inference across unseen environmental states. Experimental validation on a real-world hardware prototype confirms that our proposed framework significantly improves generalization performance, compared to the state-of-the-art solutions.

Problem

• emergent communication
• agentic AI networking
• generalization bounds
• information-theoretic foundation
• physical networking constraints

Innovation

• emergent communication
• distributed information bottleneck
• agentic AI networking
• generalization bounds
• joint optimization