# Gamma-World：超越双人的生成式多智能体世界建模

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-27 08:00
- AIHOT 分数：55
- AIHOT 链接：https://aihot.virxact.com/items/cmpovigyt09hxslv4m7sfr3ky
- 原文链接：https://arxiv.org/abs/2605.28816

## AI 摘要

针对交互式视频生成世界模型多局限于单一智能体的现状，本文提出了Gamma-World，一个面向交互式模拟的生成式多智能体世界模型。模型设计了Simplex Rotary Agent Encoding，将AI智能体表示为旋转角空间中的正单形顶点，实现无参数扩展的独立可控制性与置换对称性。为降低计算开销，提出Sparse Hub Attention，通过可学习的枢纽token中介跨智能体交互，将注意力复杂度从二次降至线性。此外，通过将全上下文扩散模型蒸馏为因果模型，结合KV缓存实现了24 FPS的实时动作响应式生成。实验表明，该模型在视频保真度、动作可控性与智能体间一致性上优于基线方案，并能从双人场景泛化至四人场景而无需额外训练。

## 正文

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.