发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多智能体世界模型的种群可扩展性问题，提出无需重训练即可扩展至任意智能体数量的Khora模型，通过解耦世界状态演化与渲染实现跨视图一致性，验证了泛化性并构建了实时交互系统。
AI 中文摘要
世界模型近期在视觉预测与交互式生成领域取得了显著进展，但将其扩展至多智能体环境会面临根本性的可扩展性挑战。现有方法通常假设训练与推理阶段的智能体数量固定，这使得模型与预设的智能体种群绑定，限制了推理阶段的可扩展性。我们的核心洞见是：跨视图一致性应源自共享的世界状态，该状态的演化不依赖预设的智能体数量，而智能体的特定观测应通过统一渲染接口查询该状态生成。基于此，我们提出Khora，这是一种可扩展的多智能体世界模型，支持推理阶段扩展至任意数量的智能体且无需重新训练。我们的框架将世界状态演化与视觉渲染解耦，并引入与种群规模无关的渲染机制来整合其他智能体的信息。该设计通过共享世界状态而非在昂贵的视频生成器内部通过观测流间的密集交互来维持跨视图一致性，实现了与查询视图数量近似线性的实际扩展性。定性实验表明，我们的方法可泛化至未见过的智能体数量，同时保持视觉质量与多智能体一致性。我们还实现了一个实时交互式系统，以展示可扩展的开放世界模拟。
英文摘要
World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent environments introduces a fundamental scalability challenge. Existing methods generally assume a fixed number of agents during training and inference, which ties the model to a pre-determined agent population and limits inference-time scalability. Our key insight is that cross-view consistency should arise from a shared world state whose evolution does not assume a predefined number of agents, while agent-specific observations should be generated by querying this state through a unified rendering interface. Based on this insight, we propose Khora, a scalable multi-agent world model that supports inference-time expansion to arbitrary numbers of agents without retraining. Our framework decouples world-state evolution from visual rendering and introduces a population-agnostic rendering mechanism for incorporating other agent information. This design maintains cross-view consistency through the shared world state rather than through dense interactions among observation streams inside the expensive video generator, enabling approximately linear practical scaling with the number of queried views. Qualitative experiments demonstrate that our approach generalizes to unseen numbers of agents while maintaining visual quality and multi-agent consistency. We further implement a real-time interactive system to demonstrate scalable open-world simulation.
CommentsTechnical report. Project page: https://rhos.ai/research/khora. Online demo: https://ophilus.ai/khora
