Yingnian Wu: Cognitive Maps: Planning-Centric World Models from Neuroscience

AI world models typically focus on prediction, treating planning as expensive downstream inference. Hippocampal cognitive maps suggest an alternative: representations whose primary purpose is making planning computationally trivial. I present a framework where place cell populations encode multi-scale transition probabilities through geometric structure. Inner products between neural embeddings directly represent how easily one can reach any location from another, transforming navigation into simple gradient ascent—no search trees, no rollouts needed. A time-scale parameter naturally creates hierarchical representations from fine-grained local precision to coarse-grained global connectivity. Non-negativity constraints induce emergent sparsity without regularization, while efficient recursive composition enables "preplay"—discovering shortcuts before physical exploration. I discuss implications for language models, vision systems, and agent architectures, arguing that planning-ready geometric representations—not just predictive models—are essential for flexible goal-directed behavior in AI systems.