{"ID":6267737,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-11T18:20:13.703712842Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07846","arxiv_id":"2607.07846","title":"VectorizationLLM: Smart Vectorization Based AI Assistant","abstract":"VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectorization, time/wave vector analysis, piecewise functions, Fourier analysis, and differential equations in MATLAB. The course application is CTEC 247: Applied Computational Analysis II by the Department of Electrical \u0026 Computer Engineering Technology at New York Institute of Technology Old Westbury. The LLM model is designed to be an instructive assistant, providing detailed explanations of concepts with examples from in-class notes without providing direct answers to questions. The model is designed with a RAG (Retrieval Augmented Generation) knowledge base and system prompt architecture. Examples in both code, text, and images are provided in the LLM responses.","short_abstract":"VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectorization, time/wave vector analysis, piecewise functions, Fourier analysis, and differential equations in MATLAB. The course application is CTEC 247: Applied Computationa...","url_abs":"https://arxiv.org/abs/2607.07846","url_pdf":"https://arxiv.org/pdf/2607.07846v1","authors":"[\"Ryan Duke\"]","published":"2026-07-08T18:27:39Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CY\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}