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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.22541 |
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| _version_ | 1866913059132407808 |
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| author | He, Mingfeng Jiang, Fayu Jiao, Junkun Li, Mingrun Li, Ke Liao, Yipu Liu, Beijiang Liu, Tong Qi, Fazhi Shang, Zijie Song, Weimin Sun, Yue Wang, Xiongfei Wang, Hong Xiong, Dongbo Yuan, Changzheng Zhang, Bolun Zhang, Zhengde Zhu, Xuliang |
| author_facet | He, Mingfeng Jiang, Fayu Jiao, Junkun Li, Mingrun Li, Ke Liao, Yipu Liu, Beijiang Liu, Tong Qi, Fazhi Shang, Zijie Song, Weimin Sun, Yue Wang, Xiongfei Wang, Hong Xiong, Dongbo Yuan, Changzheng Zhang, Bolun Zhang, Zhengde Zhu, Xuliang |
| contents | High Energy Physics (HEP) experiments like BESIII produce petabyte-scale data. Extracting physics results requires complex workflows (simulation, reconstruction, statistical analysis, etc.) that traditionally take experts months or years. Current manual methods are labor-intensive, prone to bias, and limit large-scale systematic scans. As data grows, this paradigm slows discovery. Large Language Models (LLMs) offer a solution. Their natural language understanding and code generation capabilities allow them to interpret scientific tasks and integrate with HEP tools (e.g., ROOT, BOSS) to act as an "AI partner" for autonomous analysis. We present Dr.Sai, an LLM-powered multi-agent system that translates natural language into rigorous physics workflows. As validation, Dr.Sai performed large-scale re-measurements of ten J/psi decay branching fractions - without manual coding. It successfully navigated the real BESIII computing environment and produced results matching established benchmarks. The article details Dr.Sai's architecture, the validation results, and performance evaluation. This work provides a blueprint for autonomous discovery, with relevance to other data-intensive fields like astronomy and genomics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22541 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dr.Sai: An agentic AI for real-world physics analysis at BESIII He, Mingfeng Jiang, Fayu Jiao, Junkun Li, Mingrun Li, Ke Liao, Yipu Liu, Beijiang Liu, Tong Qi, Fazhi Shang, Zijie Song, Weimin Sun, Yue Wang, Xiongfei Wang, Hong Xiong, Dongbo Yuan, Changzheng Zhang, Bolun Zhang, Zhengde Zhu, Xuliang High Energy Physics - Experiment High Energy Physics (HEP) experiments like BESIII produce petabyte-scale data. Extracting physics results requires complex workflows (simulation, reconstruction, statistical analysis, etc.) that traditionally take experts months or years. Current manual methods are labor-intensive, prone to bias, and limit large-scale systematic scans. As data grows, this paradigm slows discovery. Large Language Models (LLMs) offer a solution. Their natural language understanding and code generation capabilities allow them to interpret scientific tasks and integrate with HEP tools (e.g., ROOT, BOSS) to act as an "AI partner" for autonomous analysis. We present Dr.Sai, an LLM-powered multi-agent system that translates natural language into rigorous physics workflows. As validation, Dr.Sai performed large-scale re-measurements of ten J/psi decay branching fractions - without manual coding. It successfully navigated the real BESIII computing environment and produced results matching established benchmarks. The article details Dr.Sai's architecture, the validation results, and performance evaluation. This work provides a blueprint for autonomous discovery, with relevance to other data-intensive fields like astronomy and genomics. |
| title | Dr.Sai: An agentic AI for real-world physics analysis at BESIII |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2604.22541 |