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The introduction of AutoData, an agent that searches directly over executable selection algorithms, suggests that data engineering can be treated as an agentic machine learning problem, extending autonomous research from model and training-code optimization to the data.
DiscoPER not only automates hypothesis generation but also self-analyzes its discoveries, revealing hidden patterns and expanding the search space in unprecedented ways.
LLMs can revise scientific papers to significantly improve their predicted citation impact and perceived quality, suggesting a powerful new tool for authors to refine their manuscripts.
