Evolving with Open Worlds: Towards Recursive Self-Improvement for Future Forecasting

Key points

• Yet studying RSI requires an environment that is continuously changing, objectively verifiable, and naturally grounded, posing a fundamental challenge for agent evolution.1
• Here we introduce DeepEvolve, a sustainable evolution agent system grounded in future event prediction, in which agents continuously interact with a dynamically evolving environment and improve by internalizing and externalizing the experience gained through these interactions.1
• A living environment synthesizes verifiable tasks, and a recursive-evolution engine turns the reasoning trajectory behind each prediction back into capability: decoupling search from prediction adapts parameter-level evolution to the contamination-prone live environment, while distilling reusable skills lets the agent evolve from the limited live trajectories such an environment affords.1
• More broadly, these results suggest that a system built for self-improvement is best understood as an agent that co-evolves with a genuinely living, open-ended environment, pointing to a shift in AI from training static models on fixed data toward cultivating agents that grow stronger from their own experience, with such an open world as the indispensable basis of that growth.1

Sources (2)

• [1]Evolving with Open Worlds: Towards Recursive Self-Improvement for Future Forecasting
  Hugging Face Daily Papers · Oct 21, 12:00 AM

  Most contemporary artificial intelligence (AI) systems are released with frozen parameters and improve through external updates, whereas an ideal system would keep improving from its own experience, a capacity we call recursive self-improvement (RSI).

  Yet studying RSI requires an environment that is continuously changing, objectively verifiable, and naturally grounded, posing a fundamental challenge for agent evolution.

• [2]Evolving with Open Worlds: Towards Recursive Self-Improvement for Future Forecasting
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 02:09 PM · same content

Extractive summary: sentences quoted from the sources.