ShinkaEvolve produced algorithms that found a state-of-the-art Circle Packing solution.
We introduce ShinkaEvolve, an evolutionary code optimization framework, which discovers new algorithms with LLMs and achieves unprecedented sample efficiency. In the above on the left, we demonstrate the progress of the challenging Circle Packing task, and visualize the path of evolutionary program search to the best program on the right.
At Sakana AI, we are inspired by nature’s principles of evolution and collective intelligence to build the future of artificial intelligence. Evolution in nature is a masterful search algorithm, creating sophisticated solutions over millennia. In our work, from Evolutionary Model Merge, LLM², The AI Scientist, Automating the Search for Artificial Life, to the Darwin Gödel Machine, our consistent theme is to bring this incredible search algorithm to AI-driven discovery.
High-level overview of ShinkaEvolve. The ShinkaEvolve framework constructs an archive of evaluated programs, generates new programs, and evaluates their fitness.
ShinkaEvolve provides a sample-efficient alternative to AlphaEvolve and outperforms its Circle Packing solution.
Modern evolutionary approaches using LLMs (e.g. AlphaEvolve) have shown great promise for scientific discovery. However, they suffer from a critical limitation: they are incredibly sample inefficient, often requiring thousands of attempts to find good solutions. This makes them slow, expensive, and inaccessible to many. We wanted to change that.
In our new work, “ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution,” we introduce a new framework that leverages LLMs to evolve programs and discover new solutions with state-of-the-art performance and incredible efficiency.
We have published our technical report (https://arxiv.org/abs/2509.19349), and open-sourced our project. The code is incredibly easy to use, and we encourage you to try it out yourself:
GitHub Project: https://github.com/SakanaAI/ShinkaEvolve
Paper: https://arxiv.org/abs/2509.19349
The Japanese word ‘Shinka’ (進化) means ‘evolution’ or ‘gradual development’. ShinkaEvolve1 is an open-source framework (Apache 2.0 License) designed from the ground up to tackle the critical limitations of existing approaches: poor sample efficiency and their closed-source nature. We tested ShinkaEvolve across four completely different domains, and the results demonstrate its power, generality, and efficiency:
ShinkaEvolve discovered a new state-of-the-art solution for the classic 26-circle packing problem using only 150 samples. As you can see in the chart from our paper, this is a massive leap in efficiency compared to prior work. The discovered algorithm is a sophisticated hybrid of a golden-angle spiral initialization, gradient-based refinement, and simulated annealing to escape local optima.
Circle Packing: ShinkaEvolve's discovered solution outperforms AlphaEvolve's solution in only 150 generations.
Evolution tree of programs produced by ShinkaEvolve over several generations.