DRIVE: Dual Reflection with Interleaved Visual Evidence for Embodied Pointing

A model may identify the intended visual target yet predict a coordinate that falls elsewhere. We present DRIVE (Dual Reflection with Interleaved Visual Evidence), a two-stage reinforcement-learning framework designed to strengthen the connection between spatial reasoning and coordinate prediction. A shared policy first proposes target points, then reflects on their placement through rendered feedback: it states a spatial expectation before rendering and diagnoses the observed marker afterward. When the diagnosis identifies a mismatch, the policy revises the coordinates and inspects them again. Training uses final localization outcomes to optimize a selected response conditioned on this reflection history. On the 300-example VABench-P test set, the complete protocol achieves 71.67% final-hit accuracy, compared with 67.33% for direct inference with the same trained checkpoint. Inference ablations examine how spatial expectations, placement diagnoses, and rendered markers contribute to the trained policy’s predictions.