Contrastive Representations for Temporal Reasoning
In classical AI, perception relies on learning spatial representations, while planning—temporal reasoning over action sequences—is typically achieved through search. We study whether such reasoning can instead emerge from representations that capture both spatial and temporal structure. We show that standard temporal contrastive learning, despite its popularity, often fails to capture temporal structure, due to reliance on spurious features. To address this, we introduce Contrastive Representati