Generalization of LLM-based network agents across problem and topology changes

Determine whether large language model (LLM)-based agents that can solve a specific networking problem within a particular network topology perform equally well when the problem instance, the location in the network, or the topology itself changes in real-world deployments. This aims to assess the robustness and generalization capabilities of LLM agents beyond static, manually curated benchmarks in network operations.

Background

The paper argues that current evaluations of LLM-based agents for networking rely on small, static benchmarks crafted by domain experts, which can introduce biases and risks of data contamination. Such limitations undermine confidence in whether observed performance will transfer to varied real-world scenarios. The authors highlight the need for dynamic, scalable benchmarks that better capture the diversity of network tasks and conditions.

Within this context, the authors explicitly note uncertainty regarding the ability of agents that succeed on specific tasks within particular topologies to maintain performance when core aspects of the task or environment change. This concern motivates their framework for dynamic benchmark generation and emulator-integrated evaluation designed to probe generalization across diverse settings.