Self-Evolving Grounding World Model for Dynamic Long-Horizon Aerial Object Goal Navigation

Object-goal aerial navigation has witnessed rapid emergence with the development of vision–language–action models. However, dynamic long-horizon object-goal aerial navigation remains challenging: with goals arriving dynamically one after another, the model's current decision basis is repeatedly invalidated and a fresh direction must be determined entirely from its own observations, and as the chain grows long, the scene state and the value of earlier observations dynamically shift along the progressively revealed sequence and must be maintained rather than reset. Existing methods follow transient per-step observations with no direction signal and discard observation memory across goals, and therefore fail to handle the changing goals and to sustain success across the chain. To address these, we propose the Self-Evolving Grounding World Model (SE-GWM), a unified framework that couples goal grounding with memory evolution. Specifically, we introduce three collaborative modules: i) a **multi-view video grounding** module that performs *visual sequence grounding* over the current observations and remembered keyframes, converting each described object into an actionable 3-D goal estimate together with target, exploration, and avoidance judgments; ii) an **exploitation–exploration navigation** module that turns the grounded state into discrete flight actions through an evidence-bound planner and a goal-conditioned policy; and iii) **priority-gated memory evolution**, which preserves grounded evidence under a decaying admission threshold and feeds remembered keyframes back for re-grounding, letting the world model self-evolve with its own flight experience. Extensive experiments on our newly constructed UAV-ON-MG benchmark (1,000 episodes, 5,744 goals, 14 open-world scenes) show that SE-GWM consistently outperforms state-of-the-art methods.