Berkeley AI Research

Researchers spend hours and hours hand-crafting the strategies behind LLM-driven optimization systems like AlphaEvolve: deciding which ideas to reuse, when to explore vs exploit, and what mutations to try. 🤖But what if AI could evolve its own evolution process? We introduce EvoX, a meta-evolution pipeline that lets AI evolve the strategy guiding the optimization. It achieves high-quality solutions for <$5, while existing open systems and even Claude Code often cost 3-5× more on some tasks. Across ~200 optimization problems, EvoX delivers the strongest overall results: often outperforming AlphaEvolve, OpenEvolve, GEPA, and ShinkaEvolve on math and systems tasks, exceeding human SOTA, and improving median performance by up to 61% on 172 competitive programming problems. 👇

AlphaEvolve proved LLMs can discover novel algorithms, but it remains closed-source, and open-source alternatives (OpenEvolve, GEPA) rely on rigid, static search policies. Introducing AdaEvolve: a fully adaptive evolutionary algorithm that dynamically adjusts its own search strategy based on observed progress. It matches or beats AlphaEvolve and best known Human SOTA on math and systems benchmarks, and boosts Frontier-CS median scores by 33% over the best open-source baseline across 185 tasks. 🧵👇 (1/n)

AlphaEvolve proved LLMs can discover novel algorithms, but it remains closed-source, and open-source alternatives (OpenEvolve, GEPA) rely on rigid, static search policies. Introducing AdaEvolve: a fully adaptive evolutionary algorithm that dynamically adjusts its own search strategy based on observed progress. It matches or beats AlphaEvolve and best known Human SOTA on math and systems benchmarks, and boosts Frontier-CS median scores by 33% over the best open-source baseline across 185 tasks. 🧵👇 (1/n)