GATE: Gateway-Aware Topological Execution for Globally Coherent Zero-Shot Navigation under Partial Observability

Long-horizon navigation under partial observability requires decisions beyond the current sensing horizon. Existing methods either plan over observed geometry or obtain foresight by exploring, sampling, completing, or learning models of unseen space. We introduce GATE (Gateway-Aware Topological Execution), a training-free framework that reasons over the latent topological continuation of observable boundaries rather than reconstructing the hidden world. GATE converts reachable frontiers into gateways, estimates their goal-directed unseen continuation, and commits to promising transitions while executing only through verified free space. We evaluate GATE in controlled occlusion, long-horizon, and real-life locations against classical, belief-space, exploration, sampling-based, and learned planning families, including localization and phantom-obstacle corruption. Results and ablations show that unseen-topology reasoning and gateway commitment improve route coherence, robustness, and goal-reaching without training, privileged maps, or explicit hidden-map reconstruction.