Optimize_anything: A Universal API for Optimizing Any Text Parameter
A new paper presents 'optimize_anything', a universal API designed for optimizing text parameters using AI. The system demonstrates superior performance across various tasks, including significant improvements in accuracy and cost reductions. This approach unifies traditionally separate optimization tasks under a single framework, showcasing the versatility of LLM-based search methods.
- ▪The optimize_anything API achieves state-of-the-art results across six diverse optimization tasks.
- ▪It nearly triples the accuracy of Gemini Flash's ARC-AGI from 32.5% to 89.5%.
- ▪The system can reduce cloud costs by 40% through improved scheduling algorithms.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2605.19633 |
| Publication time | Wed, 20 May 2026 01:20:45 +0000 |
| Retrieval time | 2026-05-20T01:34:58.855Z |
| Last seen | 2026-05-20T01:34:58.855Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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 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 View PDF HTML (experimental) 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…
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