GeoReflex: Self-Correcting Active Perception for Ultra-High-Resolution Remote Sensing

Ultra-high-resolution (UHR) remote-sensing understanding requires integrating global context and local detail within a limited visual budget. Active-perception methods acquire evidence through dynamic cropping, but incorrect crops can leave subsequent exploration and reasoning dependent on incomplete observations. We introduce GeoReflex, an active-perception framework that uses reasoning to examine and correct multi-step visual observations. It connects evidence diagnosis with observation adjustment: when evidence is insufficient, the model can restore the parent view and use its diagnosis to redirect exploration. To learn this behavior, we construct GeoReflex-48K with $47,655$ examples and combine the capabilities of a Generator, Reflector, and Refiner in a single model through supervised fine-tuning and reflection-efficient reinforcement learning, teaching when to reflect and how to correct. Across three remote-sensing benchmarks, GeoReflex exceeds the strongest compared external baselines on RSHR-Bench and XLRS-Bench by $5.40$ and $7.04$ points, respectively, demonstrating the value of recovery-oriented post-training for UHR understanding. Our anonymous repository is available at https://anonymous.4open.science/r/GeoReflex-CB14/.