Gamma-World: Simplex Agent Encoding and Hub Attention for Multi-Agent World Models

Gamma-World: Simplex Agent Encoding and Hub Attention for Multi-Agent World Models Paper Code Project Most interactive video world models still assume a single agent: one user, one action stream, one generated future. γ-World takes on the harder and more realistic setting: several independently acting agents sharing the same evolving world. This is essential for games, robotics, embodied AI, social simulation, and agent training environments, where the key problem is not only visual fidelity, but whether multiple agents can act, interact, and remain consistent over time. The paper’s central contribution is a clean multi-agent design for generative world modeling. It introduces Simplex Rotary Agent Encoding to represent agent identities without fixed slots or arbitrary ordering, Sparse Hub Attention to let agents exchange information without expensive all-to-all attention, and a teacher-student distillation setup that turns a full-context diffusion model into a causal streaming model.…