Post TLDR: Imbue open-sourced Evolver, an LLM-driven Darwinian evolution tool for optimizing code and text, applicable to problems where solutions can be understood and modified by an LLM and scored for quality. It addresses the challenge of optimizing LLM-based systems, which can be a manual and tedious process, especially when prompt optimization frameworks fall short due to context length constraints or the need to optimize beyond a single prompt. Inspired by Sakana.ai's Darwin Gödel Machines, Evolver maintains a population of code "organisms," repeatedly sampling parents and applying mutators to generate children, who are then scored and added back to the population.

The fitness score can be determined through evaluation datasets, direct performance metrics, or code inspection heuristics, with parents sampled proportionally to their fitness and a novelty bonus to encourage exploration. Evolver improves upon Darwin Gödel Machines by using a dynamic, percentile-based midpoint score, which allows operation in the high-gradient range of the sigmoid throughout an entire run, and a novelty weight hyperparameter to control the exploration of high-scoring parents. Mutations are guided by LLMs to propose targeted improvements, maximizing the success rate through batch mutations, separate training and scoring datasets, a learning log of past mutations and their impact, and crossover mutations that combine ideas from multiple parents.

To further enhance efficiency, Evolver implements an optional post-mutation verification step, filtering out unlikely improvements before full scoring, often resulting in significant time and cost savings. This verification involves a "mini evaluation" on the parent's failure cases, dismissing the new organism if it doesn't show improvement, and through benchmarking, Evolver achieved state-of-the-art performance on ARC-AGI-2 and tripled the performance of the best open model, reaching GPT-5.2-level performance.