Augmenting Human Research Insight Using Large Language Models

This paper presents a lightweight assistant for coding theory research. The system queries the arXiv repository, retrieves metadata and abstracts, applies large language model (LLM) summarization, and stores results in CSV files. We describe the code structure, execution, and outputs. We also outline the vision of this prototype as part of a larger research infrastructure
for information theory.