AI

Iterated creation, grading, and revision of data science projects by language models

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Briefings

This study evaluates how agentic AI models improve data science projects through iterative cycles of writing, grading, and revision. Researchers tested seven models from five developers across 49 writer-grader pairs over three rounds. Initial submissions scored 59% average, rising to 76% after feedback. Feedback rounds yielded the highest marks, though some models showed gains from revision alone. The study found that weaker models generally fail to improve stronger models, and about half of observed score differences stem from grader noise.

Key points

• Seven models from five developers (Anthropic, OpenAI, Google, DeepSeek, Z.AI) were tested across 49 writer-grader pairs.
• Initial project scores averaged 59%, increasing to 76% after three rounds of feedback and revision.

Context

The research investigates agentic AI capabilities in end-to-end data science workflows, specifically measuring improvements in project completion and quality through iterative feedback loops. This analysis provides insight into how multi-step AI agents can autonomously refine outputs, which is critical for evaluating the scalability and reliability of agentic systems in professional data science contexts.

Limitations

The study covers only seven models from five developers and runs each pair three times. Results may vary with different model configurations or datasets. Grader noise accounts for about half of observed score differences, limiting generalizability. Preprint status (arXiv:2611.05548v1) indicates uncertainty regarding final validation and reproducibility.

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