AI-Powered Assistant for Long-Term Access to RHIC Knowledge
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916947185106944 |
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| author | Atif, Mohammad Garonne, Vincent Lancon, Eric Lauret, Jerome Prozorov, Alexandr Vranovsky, Michal |
| author_facet | Atif, Mohammad Garonne, Vincent Lancon, Eric Lauret, Jerome Prozorov, Alexandr Vranovsky, Michal |
| contents | As the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory concludes 25 years of operation, preserving not only its vast data holdings ($\sim$1 ExaByte) but also the embedded scientific knowledge becomes a critical priority. The RHIC Data and Analysis Preservation Plan (DAPP) introduces an AI-powered assistant system that provides natural language access to documentation, workflows, and software, with the aim of supporting reproducibility, education, and future discovery. Built upon Large Language Models using Retrieval-Augmented Generation and the Model Context Protocol, this assistant indexes structured and unstructured content from RHIC experiments and enables domain-adapted interaction. We report on the deployment, computational performance, ongoing multi-experiment integration, and architectural features designed for a sustainable and explainable long-term AI access. Our experience illustrates how modern AI/ML tools can transform the usability and discoverability of scientific legacy data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Powered Assistant for Long-Term Access to RHIC Knowledge Atif, Mohammad Garonne, Vincent Lancon, Eric Lauret, Jerome Prozorov, Alexandr Vranovsky, Michal Information Retrieval Artificial Intelligence Computation and Language As the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory concludes 25 years of operation, preserving not only its vast data holdings ($\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. |
| title | AI-Powered Assistant for Long-Term Access to RHIC Knowledge |
| topic | Information Retrieval Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.09688 |