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arXiv Machine Learning By Cheikh Ahmed, Zhiyu Zhang, Kun Mao, Yong Zhang, Zirui Zhou

Organon : From Evolutionary Search to Recursive Self-Improving Agents for Scientific Discovery

Read the original on arXiv Machine Learning →

Organon is a recursively self‑improving agent designed for scientific discovery that autonomously revises its search strategies, population management, diagnostics, memory, and worker coordination during execution. By diagnosing and correcting its own adaptation procedures, the agent continuously refines the mechanisms guiding its future self‑improvement. Across 21 computational discovery tasks—including algorithm design, GPU kernel optimization, and systems optimization—Organon outperforms competing frameworks, achieving a 20.5% mean performance gain on ALE‑Bench Lite and up to 1.67× speedups on KernelBench.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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