AgentGarten: Code Worlds for Evolving Agents
- Published
- Source
- arXiv
- Paper number
- 1182
- Field
- Computer Vision
- arXiv ID
- 2610.12374
Key points
- A division of labor where the engine handles state and rules while a neural renderer handles only visuals lets new virtual worlds be built entirely in code without 3D assets.
- The Adversarial Forcing technique distills a pretrained video model into a real-time renderer, keeping visual quality from collapsing over long rollouts.
- Agents learned strategies like shelter building in just 4 rounds, millions of times fewer than conventional reinforcement learning.
- Agents distilled their experience into written playbooks that subsequent agents inherited and refined, producing evolution across generations.
- Coding agents can automatically generate and extend new worlds from a single image or text description, securing environment scalability.
Paper links
External research summaries. These are not HDATF publications or measured product results.