82 citations · 142 across the 6 of their papers we have counts for
7 papers
Reliable LLM-Generated Programs for High-Energy Physics Experiments through Graph-Grounded Software Knowledge
Yue Sun, Tong Liu, Yipu Liao +2
Extracting physics information from modern particle-physics experiments requires multistage analyses implemented on top of large and highly interconnected software ecosystems. Gene…
HepScript: A Dual-Use DSL for Human-AI Collaborative Data Analysis Workflows in High-Energy Physics
Junkun Jiao, Tong Liu, Ke Li +6
The escalating data scale in High-Energy Physics (HEP) fuels a growing aspiration for higher analytical efficiency. While Large Language Models (LLMs) offer a path toward automatio…
Dr.Sai: An agentic AI for real-world physics analysis at BESIII
Mingfeng He, Fayu Jiang, Junkun Jiao +16
High Energy Physics (HEP) experiments like BESIII produce petabyte-scale data. Extracting physics results requires complex workflows (simulation, reconstruction, statistical analys…
A Design Science Blueprint for an Orchestrated AI Assistant in Doctoral Supervision
Teo Susnjak, Timothy R. McIntosh, Tong Liu +1
This study presents a design science blueprint for an orchestrated AI assistant and co-pilot in doctoral supervision that acts as a socio-technical mediator. Design requirements ar…
Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence
Teo Susnjak, Timothy R. McIntosh, Andre L. C. Barczak +4
In this study, we explored the progression trajectories of artificial intelligence (AI) systems through the lens of complexity theory. We challenged the conventional linear and exp…
From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Models
Timothy R. McIntosh, Teo Susnjak, Tong Liu +3
This study investigated the integration readiness of four predominant cybersecurity Governance, Risk and Compliance (GRC) frameworks - NIST CSF 2.0, COBIT 2019, ISO 27001:2022, and…
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence
Timothy R. McIntosh, Teo Susnjak, Nalin Arachchilage +3
The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred public curiosity to evaluate and compare different LLMs, leading many researcher…