[2605.19633] optimize_anything: A Universal API for Optimizing any Text Parameter

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Computer Science > Computation and Language

arXiv:2605.19633 (cs)

[Submitted on 19 May 2026]

Title:optimize_anything: A Universal API for Optimizing any Text Parameter

Authors:Lakshya A Agrawal, Donghyun Lee, Shangyin Tan, Wenjie Ma, Karim Elmaaroufi, Rohit Sandadi, Sanjit A. Seshia, Koushik Sen, Dan Klein, Ion Stoica, Joseph E. Gonzalez, Omar Khattab, Alexandros G. Dimakis, Matei Zaharia<br>View a PDF of the paper titled optimize_anything: A Universal API for Optimizing any Text Parameter, by Lakshya A Agrawal and 13 other authors

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Abstract:Can a single LLM-based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are formulated as improving a text artifact evaluated by a scoring function, a single AI-based optimization system-supporting single-task search, multi-task search with cross-problem transfer, and generalization to unseen inputs-achieves state-of-the-art results across six diverse tasks. Our system discovers agent architectures that nearly triple Gemini Flash's ARC-AGI accuracy (32.5% to 89.5%), finds scheduling algorithms that cut cloud costs by 40%, generates CUDA kernels where 87% match or beat PyTorch, and outperforms AlphaEvolve's reported circle packing solution (n=26). Ablations across three domains reveal that actionable side information yields faster convergence and substantially higher final scores than score-only feedback, and that multi-task search outperforms independent optimization given equivalent per-problem budget through cross-task transfer, with benefits scaling with the number of related tasks. Together, we show for the first time that text optimization with LLM-based search is a general-purpose problem-solving paradigm, unifying tasks traditionally requiring domain-specific algorithms under a single framework. We open-source optimize\_anything with support for multiple backends as part of the GEPA project at this https URL .

Comments:<br>16 pages, 11 figures; Blog: this https URL

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Software Engineering (cs.SE)

MSC classes:<br>68T05, 68T07, 68T20, 68T50, 68W50, 90C26, 90C59, 52C15

ACM classes:<br>I.2.6; I.2.7; I.2.8; I.2.11; D.1.2; D.2.2; G.1.6; F.2.2

Cite as:<br>arXiv:2605.19633 [cs.CL]

(or<br>arXiv:2605.19633v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.19633

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arXiv-issued DOI via DataCite (pending registration)

Journal reference:<br>Proceedings of the ACM Conference on AI and Agentic Systems (CAIS 26), May 26-29, 2026, San Jose, CA, USA

Related DOI:

https://doi.org/10.1145/3786335.3813167

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DOI(s) linking to related resources

Submission history<br>From: Lakshya A Agrawal [view email]<br>[v1]<br>Tue, 19 May 2026 10:18:12 UTC (3,492 KB)

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