Generalization Bounds of Emergent Communications for Agentic AI Networking

TL;DR

This paper introduces an information-theoretic emergent communication framework for agentic AI networking, providing theoretical bounds and experimental validation to improve generalization in decentralized multi-agent systems.

Contribution

It proposes a novel joint loss function and leverages DIB theory to quantify trade-offs, offering the first generalization bounds for emergent communication protocols in agentic AI networks.

Findings

Significant improvement in generalization performance over state-of-the-art solutions.

Theoretical bounds on emergent communication protocol performance during decentralized inference.

Validation on real hardware confirms practical effectiveness.

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.