用离散网格建模环境，让智能体规划更稳定可靠。
The Global Neural World Model: Spatially Grounded Discrete Topologies for Action-Conditioned Planning
- 通过连续熵约束实现拓扑量化，用网格对齐纠正误差。
- 自回归推理中避免流形漂移，支持长期规划。
- 适合需要空间推理与因果发现的强化学习任务。
我们提出全局神经世界模型（GNWM），一种自稳框架，通过平衡连续熵约束实现拓扑量化。作为连续、动作条件的联合嵌入预测架构（JEPA），GNWM将环境映射至离散2D网格，强制平移等变性，无需像素级重建。实验表明，该架构在自回归推演中通过网格'对齐'机制防止流形漂移。通过最大熵探索（随机游走）训练，模型学习通用转移动态而非记忆特定专家轨迹。我们在被动观测、主动代理控制和抽象序列场景中验证了GNWM，证明其不仅能作为空间物理模拟器，还能作为因果发现模型，将连续可预测概念组织为结构化拓扑地图。
原文摘要 · Abstract (English)
We present the Global Neural World Model (GNWM), a self-stabilizing framework that achieves topological quantization through balanced continuous entropy constraints. Operating as a continuous, action-conditioned Joint-Embedding Predictive Architecture (JEPA), the GNWM maps environments onto a discrete 2D grid, enforcing translational equivariance without pixel-level reconstruction. Our results show this architecture prevents manifold drift during autoregressive rollouts by using grid ``snapping'' as a native error-correction mechanism. Furthermore, by training via maximum entropy exploration (random walks), the model learns generalized transition dynamics rather than memorizing specific expert trajectories. We validate the GNWM across passive observation, active agent control, and abstract sequence regimes, demonstrating its capacity to act not just as a spatial physics simulator, but as a causal discovery model capable of organizing continuous, predictable concepts into structured topological maps.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。