This paper introduces 'Autonomous Agentic Data Engineering,' a novel task to evaluate LLMs as autonomous data engineers for model specialization through end-to-end data curation. Experiments show autonomous LLM data engineers achieve substantial gains, with GPT-5.2 improving a student model by 57.29%.
This paper introduces GEM (Geometric Entropy Mixing), a novel framework for LLM data curation that reformulates the problem as a variational one on the hypersphere. GEM optimizes data composition for LLM pre-training, overcoming categorization flaws and discovering balanced semantic structures.
Generalist coding agents show potential in automating the labor-intensive process of data curation for AI development, as tested on the new Curation-Bench benchmark. While agents achieve strong baselines, an "execution-research gap" is observed where they primarily refine existing policies instead of exploring novel approaches.