Title: ShinkaEvolve: Towards Open-Ended and Sample-Efficient Program Evolution
Primary area: foundation or frontier models, including LLMs
Keywords: Evolution, LLMs, Scientific discovery

Abstract: We introduce ShinkaEvolve: a new framework leveraging large language models (LLMs) to advance scientific discovery with state-of-the-art performance and efficiency. The field of LLM-driven scientific discovery has seen significant progress, but has yet to overcome a critical limitation: sample inefficiency, requiring thousands of samples to identify effective solutions. ShinkaEvolve takes a concrete step towards addressing this critical limitation by introducing three key innovations: a parent sampling technique balancing exploration and exploitation, code novelty rejection-sampling for efficient search space exploration, and a bandit-based LLM ensemble selection strategy. When applied to the canonical circle-packing optimization task, ShinkaEvolve discovers a new state-of-the-art circle packing solution using only 150 samples, orders of magnitude fewer than prior frameworks. Furthermore, applied to a broader set of engineering problems, ShinkaEvolve designs robust agentic harnesses for AIME mathematical reasoning tasks, identifies improvements to ALE-Bench competitive programming solutions, and discovers novel mixture-of-expert load balancing loss functions to stabilize LLM training itself. We provide ShinkaEvolve's full code together with this submission, which will be open-sourced to accelerate open advancements to open-ended automated discovery across diverse computational problems.

TL;DR: We introduce a new framework using LLMs to advance automated scientific discovery with SotA efficiency and significant results across engineering and scientific fields.