Initialization and Rate-Quality Functions for Generative Network Layer Protocols

TL;DR

This paper introduces a new initialization protocol for learning rate-quality functions in GenAI-enabled networks, enabling efficient approximation quality assessment over limited communication links, with practical validation showing significant gains.

Contribution

It proposes a novel, protocol-agnostic method for learning rate-quality functions in GenAI networks, enhancing communication efficiency and approximation assessment.

Findings

Successful rate-quality estimation with as few as 2 images

Positive gains over JPEG after 1-18 post-learning transmissions

Practical, compression-agnostic foundation for GenAI-based network compression

Abstract

Generative AI (GenAI) creates full content based on compact prompts. While GenAI has been used for applications where the generated content is returned to the prompt sender, it can play a vital role in extending the capacity of communication networks by transmitting compact prompts through links with limited capacity and, then, generating and forwarding approximations from the GenAI to the destination. This poses the challenge of evaluating the quality of those approximations as a function of the rate between the source and the GenAI node, while accounting for the communication overhead of learning. We present a method- and data-agnostic initialization protocol for learning rate-quality functions in GenAI-aided networks, defining three variants: (1) source-oriented, (2) node-oriented, and (3) destination-oriented. Each of them has different messaging flows based on where quality measurements are performed. The protocol augments node discovery protocols (e.g., MCP, A2A) when sources lack confidence in advertised model performance. We illustrate operation via statistical determination of required learning data, and validate using two prompting approaches. Results show successful rate-quality estimation with as few as 2 images, and positive gains over JPEG after just 1-18 post-learning transmissions, providing a practical, compression-agnostic foundation for GenAI-based network compression.