FlockWorld: Toward Population-Scale Multi-Agent Video World Models through Artificial Life Simulation

Most action-conditioned video world models remain limited to a single player, while existing multiplayer models support only small, fixed groups of agents. We introduce FlockWorld, an artificial-life environment built on Reynolds’s Boids algorithm for studying population-scale multi-agent video world models. Specifically, FlockWorld renders synchronized ego-centric crops, assigns distinct colors to preserve camera-agent identity across views, exposes rule-derived accelerations as actions, and allows the numbers of boids and camera agents to be configured independently. We further introduce FlockDiT, an action-conditioned latent diffusion transformer trained in FlockWorld to jointly generate synchronized egocentric videos for multiple agents. Our results suggest that joint generation benefits agent detection at larger density-matched settings and that diffusion forcing improves rollout fidelity, positioning FlockWorld and FlockDiT as a controlled testbed for multi-agent video generation.