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This workshop seeks to consolidate efforts by providing an interdisciplinary forum for presenting cutting-edge research, sharing deployment experiences, and showcasing real-world systems in this rapidly evolving field of AI Data Scientist.
The OpenCity framework provides a robust and efficient framework that unlocks the potential of LLMs for large-scale, high-fidelity multi-agent systems analysis and demonstrates the framework's capability as a policy sandbox through a counterfactual analysis of urban equity.
Agent-based models (ABMs) have long been employed to explore how individual behaviors aggregate into complex societal phenomena in urban space. The rise of Large Language Models (LLMs) offers a new paradigm for ABM, enabling high-fidelity modeling of human behavior. However, urban dynamics often require extensive agent...
As data volumes and analytical demands grow, traditional data science workflows struggle to meet the need for efficiency, scalability, and reliability. The rapid advancement of large language models (LLMs) has opened new possibilities for AI-powered agents to augment or automate end-to-end data science pipelines—from d...
A systematic literature review on how RL are adapted and scaled as a fundamental post-training tools and how innovations in the RL pipeline enhance the domain-specific LLMs is conducted.
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