Many animals possess a remarkable capacity to rapidly construct flexible cognitive maps of their environments. These maps are crucial for ethologically relevant behaviors such as navigation, exploration, and planning. Existing computational models typically require long sequential trajectories to build accurate maps, but neuroscience evidence suggests maps can also arise from integrating disjoint experiences governed by consistent spatial rules. We introduce the Episodic Spatial World Model (ESWM), a novel framework that constructs spatial maps from sparse, disjoint episodic memories. Across environments of varying complexity, ESWM predicts unobserved transitions from minimal experience, and the geometry of its latent space aligns with that of the environment. Because it operates on episodic memories that can be independently stored and updated, ESWM is inherently adaptive, enabling rapid adjustment to environmental changes. Furthermore, we demonstrate that ESWM readily enables near-optimal strategies for exploring novel environments and navigating between arbitrary points, all without the need for additional training. Our work demonstrates how neuroscience-inspired principles of episodic memory can advance the development of more flexible and generalizable world models.

翻译：许多动物具备快速构建灵活环境认知地图的显著能力。这些地图对于导航、探索和规划等生态相关行为至关重要。现有计算模型通常需要长序列轨迹才能构建精确地图，但神经科学证据表明，地图亦可通过整合由一致空间规则支配的离散经验而形成。我们提出情景空间世界模型（ESWM），这是一种从稀疏、离散的情景记忆构建空间地图的新型框架。在不同复杂度的环境中，ESWM能够基于极少经验预测未观测到的状态转移，其潜在空间的几何结构与环境的几何特性保持一致。由于ESWM基于可独立存储和更新的情景记忆运行，其本质上具备适应性，能够快速响应环境变化。此外，我们证明ESWM无需额外训练即可实现探索新环境与任意点间导航的近似最优策略。本研究揭示了受神经科学启发的情景记忆原理如何推动更灵活、更具泛化能力的世界模型的发展。