The AI Picbreeder Experiment: Can AI agents be creative when nobody tells them what to create? Blog: https://pub.sakana.ai/picbreeder-vlm In our new #GECCO2026 paper, "In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models", in collaboration with MIT and NYU, we revisit Picbreeder, a lost website where people collaboratively evolved images without any predefined objective. Users simply selected images they found interesting, allowing unexpected forms such as faces, animals, vehicles, and skulls to emerge gradually across many generations and many different people. We recreated this process using vision-language model agents. The agents explore a shared archive, choose images to branch from, evolve new candidates, publish their favorites, and evaluate the creations of other agents. There is no target image and no explicit definition of what counts as progress. The results reveal both the promise and current limitations of AI-driven open-ended discovery. Compared with humans, VLM agents tend to keep circling back to the same kinds of images and concepts. They repeatedly select similar parents, make smaller conceptual leaps, and often refine an existing idea rather than abandoning it in search of something genuinely unexpected. However, introducing a diverse population of agent personalities substantially improves exploration. In some runs, diverse agent populations approached or matched the human archive on measures of semantic diversity and produced more balanced evolutionary trees. We also find intriguing evidence that open-ended evolution can produce more robust representations. A skull evolved by the agents changes smoothly when its underlying neural representation is perturbed, less fractured than a skull directly optimized with gradient descent, although still less cleanly disentangled than one evolved collectively by humans. But perhaps the most interesting result is the gap that remains. Humans appear better at turning fortunate accidents into sustained creative discoveries: recognizing when something unexpected is worth pursuing, refining it, and then making a larger conceptual leap. The AI agents often notice interesting patterns too, but are more likely to become trapped in them. We still do not fully understand what enables humans to navigate open-ended search in this way, or what ingredient(s) current AI systems are missing. For now, the results suggest that there remains something important about human creativity that AI agents have not yet learned to reproduce. This paper will be presented at #GECCO2026 and is nominated for a best paper award! Please check out the interactive blog and technical paper for more details! Read our full paper: https://arxiv.org/abs/2605.23908 🐟