Retrieval-Augmented Generation for Scientific Code Understanding
Large language models have become central to modern coding assistants, but state-of-the-art systems such as Claude Code or Codex rely on very large, cloud-hosted models with significant computational cost and data-privacy implications. This work investigates whether a useful, fully local coding agent can be built around small open-source models by shifting the computational burden away from inference. We develop a Retrieval-Augmented Generation (RAG) system for scientific code understanding that strictly separates an expensive offline ingestion stage parsing, structural graph construction, LLM-generated entity explanations, and embedding from a lightweight online answering stage. The syst...
| Authors | Aaron Nobile, Andreas Adelmann, Mohsen Sadr |
|---|---|
| Primary category | cs.SE |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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