@DivyanshT91162: Microsoft Research just dropped a paper that completely flips interpretability on its head. (bookmark this) For years, …

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interpretability agentic-ai microsoft-research tabular-data llm-reasoning machine-learning

Summary

Microsoft Research introduced Agentic-iModels, a framework where coding agents evolve scikit-learn regressors optimized for LLM interpretability rather than human readability, outperforming traditional interpretable ML methods across 65 datasets.

Microsoft Research just dropped a paper that completely flips interpretability on its head. (bookmark this) For years, interpretable ML was designed for humans reading charts, trees, and coefficients. But AI agents are now the ones analyzing models. So Microsoft asked a different question: “What if a model was optimized to be interpretable to another LLM?” That led to Agentic-iModels. An autonomous research loop where coding agents like Claude Code and Codex continuously evolve scikit-learn regressors that are: • highly accurate • simple enough for another LLM to reason about directly from text The wild part: Interpretability is tested by giving a small LLM nothing except the model’s __str__ output… and checking if it can still predict behavior, explain feature effects, and answer counterfactuals. No hidden weights. No visualizations. Just raw readable logic. Across 65 tabular datasets, these models outperformed traditional “interpretable ML” methods like decision trees, GAMs, and sparse linear models on the accuracy vs readability frontier. And when plugged into agentic data science systems on the BLADE benchmark, performance jumped by 8%–73%. This feels less like “explainable AI”… and more like the beginning of AI-native scientific reasoning.
Original Article

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