Gamma-World: 超越双人博弈的生成式多智能体世界建模

摘要

用于交互式视频生成的世界模型主要聚焦于单智能体场景，即通过单一控制信号生成未来观测结果。然而，许多生成环境需要多智能体交互：多个玩家、机器人或具身智能体在同一共享空间中同时行动。将世界模型扩展到此类场景需要设计具有原则性的多智能体架构：智能体应保持独立可控性、置换对称性，并在确保时间与视角一致性的同时支持高效推理。本文提出面向交互式仿真的生成式多智能体世界模型。该模型引入简化旋转智能体编码（Simplex Rotary Agent Encoding），这是3D RoPE的一种无参数扩展，将智能体表示为旋转角度空间中规则单纯形的顶点。这赋予每个智能体独特相位，同时使所有智能体置换等价，从而无需学习逐个槽位的身份标识或固定智能体顺序即可实现可扩展的智能体识别。为避免跨智能体的密集全连接注意力，我们进一步提出稀疏中心注意力（Sparse Hub Attention），通过可学习的中心令牌中介跨智能体的令牌交互，将跨智能体注意力计算复杂度从智能体数量的二次方降至线性。为支持实时推演，我们将全上下文扩散教师模型蒸馏为因果学生模型，该学生模型通过KV缓存顺序生成时间块，实现每秒24帧的动作响应式生成。在多玩家虚拟环境中的实验表明，与基于槽位和密集注意力的基线方法相比，本模型在视频保真度、动作可控性和智能体间一致性上均有提升，且无需额外训练即可从两玩家泛化至四玩家场景。

English

World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single control signal. However, many generated environments require multi-agent interaction: multiple players, robots, or embodied agents act simultaneously within a shared space. Scaling world models to such settings requires a principled multi-agent design: agents should remain independently controllable, permutation-symmetric, and support efficient inference while maintaining consistency across time and perspectives. In this paper, we present our generative multi-agent world model for interactive simulation. It introduces Simplex Rotary Agent Encoding, a parameter-free extension of 3D RoPE that represents agents as vertices of a regular simplex in rotary angle space. This gives each agent a distinct phase while making all agents permutation-equivalent, enabling scalable agent identity without learned per-slot identities or a fixed agent ordering. To avoid dense all-to-all attention across agents, we further propose Sparse Hub Attention, where learnable hub tokens mediate token interaction across agents, reducing cross-agent attention cost from quadratic to linear in the number of agents. For real-time rollout, we distill a full-context diffusion teacher into a causal student that generates temporal blocks sequentially with KV caching, enabling action-responsive generation at 24 FPS. Experiments in multiplayer virtual environments show that our model improves video fidelity, action controllability, and inter-agent consistency over slot-based and dense-attention baselines, while generalizing from two to four players without additional training.