One AI System Solves Scheduling, Code, and Design Problems Better
Original: optimize_anything: A Universal API for Optimizing any Text Parameter
Source: arxiv.org ↗
Who: Submitted to arXiv by lead author Lakshya A. Agrawal alongside 13 co-authors from UC Berkeley, including Matei Zaharia, Ion Stoica, Dan Klein, and Koushik Sen — a cluster of researchers behind several foundational open-source systems — plus Omar Khattab, known for work on language-model-driven programming frameworks.
What's new: The team introduces optimize_anything, a single -based system that can improve almost any solution expressed as text, without needing a specialized tool built for each problem. The core idea is that if you can write down what "good" looks like as a score, the system can search for a better version of your solution by repeatedly drafting revisions and checking the score — the way a student might rewrite an essay after seeing a rubric grade.
How it works: The system treats every problem the same way: a candidate solution is a piece of text, and a scoring function says how good it is. The proposes revisions, sees the score plus any explanatory feedback, and iterates. Crucially, the system supports multi-task search, meaning it can work on several related problems simultaneously and transfer lessons learned on one problem to speed up progress on another — similar to how studying for one history exam helps you on a related one. The researchers found that giving the model a written explanation of why a score is what it is, rather than just the number, leads to much faster improvement.
The numbers: Results across six tasks are striking. On , the system raised 's accuracy from 32.5% to 89.5%. It found scheduling algorithms that cut cloud computing costs by 40%. It generated low-level GPU code where 87% of outputs matched or beat 's performance. It also surpassed the best-known solution from on a classical geometry puzzle involving fitting circles into a fixed space.
Why it matters: The practical implication is that a developer who can write a scoring function — "does this scheduler waste less money?" — no longer needs to design a custom optimization algorithm from scratch. The same engine handles code generation, scheduling, puzzle-solving, and potentially much else. The project is open-sourced as part of the GEPA project at UC Berkeley, which means outside researchers can plug it into their own problems immediately.