LASERAgent: Toward Highly Automated and Reliable Reproduction of LEO Networking Experiments

It is still difficult to reproduce the low-Earth-orbit (LEO) networking experiments since the experimental settings are scattered and the Large Language Model (LLM) assisted workflow is laborious and needs to be refined by manually crafting the prompt, transferring the code and debugging. Existing retrieval-assisted pipelines still demand substantial human effort for parameter inference and code repair, particularly given the precise alignment required for LEO topology, constellation, and simulator configurations.

This paper presents LASERAgent, a highly automated agent system for LEO networking experiment reproduction. Unlike retrieval-assisted pipelines that mainly support evidence retrieval and code generation, LASERAgent organizes the downstream workflow into a bounded pipeline of retrieve - extract - generate - check - execute, integrating structured parameter extraction, template-guided generation, deterministic validation, controlled execution, and limited retry.

Preliminary results show that LASERAgent reduces required human interventions by roughly half while improving result consistency over prior retrieval-assisted workflows, and that the validation stage plays an important role in reliable automation.