AI-Powered Assistant for Long-Term Access to RHIC Knowledge

• URL: http://arxiv.org/abs/2509.09688v1
• Date: Mon, 18 Aug 2025 15:16:29 GMT
• Title: AI-Powered Assistant for Long-Term Access to RHIC Knowledge
• Authors: Mohammad Atif, Vincent Garonne, Eric Lancon, Jerome Lauret, Alexandr Prozorov, Michal Vranovsky,
• Abstract summary: The RHIC Data and Analysis Preservation Plan (DAPP) introduces an AI-powered assistant system that provides natural language access to documentation.<n>We report on the deployment, computational performance, ongoing multi-experiment integration, and architectural features designed for a sustainable and explainable long-term AI access.<n>Our experience illustrates how modern AI/ML tools can transform the usability and discoverability of scientific legacy data.
• Score: 35.18016233072556
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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