AI RESEARCH
CEDAR: Context Engineering for Agentic Data Science
arXiv CS.LG
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ArXi:2601.06606v2 Announce Type: replace We nstrate CEDAR, an application for automating data science (DS) tasks with an agentic setup. Solving DS problems with LLMs is an underexplored area that has immense market value. The challenges are manifold: task complexities, data sizes, computational limitations, and context restrictions. We show that these can be alleviated via effective context engineering. We first impose structure into the initial prompt with DS-specific input fields, that serve as instructions for the agentic system.