Agent Swarm Lab / Six worlds · One open question
Little minds.
Large worlds.
When does intelligence emerge from many bounded, partially informed agents?
有限的个体,不完整的世界。什么时候,群体真的会更聪明?
Choose a world ↓Each illustration is driven by an actual simulator. Move through time, inspect a body, and replay the same seed.
Move the food. Share a false belief. Lose the mother. The interesting result may be the one that refuses your story.
Delivered work, forecast error, memory, energy and messages. A beautiful swarm is a beginning, not an answer.
Roach Emergence
When does local signaling earn its energy cost?
Enter the experiment ↗FIELD 02beliefsShared Reality
Can agents agree enough to act without agreeing confidently on a mistake?
Enter the experiment ↗FIELD 03evolutionLearn, Forget, Evolve
Can a small policy change adapt to a new world without erasing the old one?
Enter the experiment ↗FIELD 04asteroidThe Asteroid Button
Does distributing a fixed budget preserve useful work after correlated damage?
Enter the experiment ↗FIELD 05oceanOcean Swarm
Who repairs the sensing constellation when the sea breaks the plan?
Enter the experiment ↗FIELD 06frontierMinimum Intelligence
How much model planning earns its place when physical work, information and complexity all count?
Enter the experiment ↗A laboratory, not a verdict
The swarm is allowed to lose.
These six experiments follow a single chain: local coordination → shared prediction → bounded adaptation → robustness → physical constraints → the minimum intelligence needed. Controllers and budgets are declared, failures are retained, and the full source and seeded results are inspectable inside each world.
All environments are synthetic. The three-dimensional sketches illustrate declared models; they do not certify biology, robotics, ocean engineering or general intelligence. Actual model responses, where available, are separated from rule-based simulation.
群体可以输,失败必须留下。六个实验从局部协作、共享预测、有界适应、抗冲击、物理约束,一直追问到最低智能需求。每个世界都有方法、源码与可重播证据。