{"ID":2881169,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.09688","arxiv_id":"2509.09688","title":"AI-Powered Assistant for Long-Term Access to RHIC Knowledge","abstract":"As the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory concludes 25 years of operation, preserving not only its vast data holdings ($\\sim$1 ExaByte) but also the embedded scientific knowledge becomes a critical priority. The RHIC Data and Analysis Preservation Plan (DAPP) introduces an AI-powered assistant system that provides natural language access to documentation, workflows, and software, with the aim of supporting reproducibility, education, and future discovery. Built upon Large Language Models using Retrieval-Augmented Generation and the Model Context Protocol, this assistant indexes structured and unstructured content from RHIC experiments and enables domain-adapted interaction. We report on the deployment, computational performance, ongoing multi-experiment integration, and architectural features designed for a sustainable and explainable long-term AI access. Our experience illustrates how modern AI/ML tools can transform the usability and discoverability of scientific legacy data.","short_abstract":"As the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory concludes 25 years of operation, preserving not only its vast data holdings ($\\sim$1 ExaByte) but also the embedded scientific knowledge becomes a critical priority. The RHIC Data and Analysis Preservation Plan (DAPP) introduces an AI-power...","url_abs":"https://arxiv.org/abs/2509.09688","url_pdf":"https://arxiv.org/pdf/2509.09688v1","authors":"[\"Mohammad Atif\",\"Vincent Garonne\",\"Eric Lancon\",\"Jerome Lauret\",\"Alexandr Prozorov\",\"Michal Vranovsky\"]","published":"2025-08-18T15:16:29Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\",\"cs.CL\"]","methods":"[\"RAG\",\"Language Model\"]","has_code":false}