Sebastian Risi

The AI Picbreeder Experiment: Can AI agents be creative when nobody tells them what to create? Blog: pub.sakana.ai/picbreeder-vlm Paper: arxiv.org/abs/2605.23908 In our #GECCO2026 paper, we revisit Picbreeder, a website where people collaboratively evolved images without any p…

In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models arxiv.org

AI Weekly's analysis →

• Researchers Sam Earle, Kai Arulkumaran, Andrew Dai, Akarsh Kumar, Julian Togelius and Sebastian Risi replicate Picbreeder with frontier vision-language models as users.
• The system's output shows 'clear qualitative differences' from the historical human baseline on phylogenetic complexity and visual and semantic salience and novelty.
• The team studies three candidate ingredients: exploratory noise in selection, behavioral diversity between agents, and memory-based narrative momentum.

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Link to pre-print: arxiv.org/abs/2607.01047 Joint work with Ane Espeseth, Eleni Nisioti, @risi.bsky.social and @stenichele.bsky.social

Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates arxiv.org View on Bluesky →

The AI Picbreeder Experiment: Can AI agents be creative when nobody tells them what to create? Blog: pub.sakana.ai/picbreeder-vlm Paper: arxiv.org/abs/2605.23908 In our #GECCO2026 paper, we revisit Picbreeder, a website where people collaboratively evolved images without any p…

The AI Picbreeder Experiment: In Search of Automatic Open-Endedness pub.sakana.ai

AI Weekly's analysis →

• Researchers from NYU, MIT and Sakana AI replaced Picbreeder's human selectors with frontier VLMs and reported clear qualitative gaps versus the historical human baseline.
• Gemini-2.5-pro topped the models tested, but archives showed 'mode collapse' without exploratory noise and 'auto-sycophantic' loops when given more history.
• Scaling to 1,000 agents widened coverage yet 10 to 20 percent of the archive turned into uninterpretable adversarial psychedelic patterns.

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