Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs

Multimodal Large Language Models (MLLMs) have demon-strated remarkable success in various Remote Sensing (RS) tasks. How-ever, their ability to comprehend negation remains underexplored, limit-ing deployment in real-world applications where models must explicitlyidentify what is false or absent, e.g., emergency responders need to locatenon-flooded routes for evacuation. To comprehensively study this limi-tation, we introduce RS-Neg, the first benchmark to evaluate negationunderstanding across region-level to scene-level tasks. Specifically, we de-sign an automated data generation pipeline for RS imagery, using LLMsto synthesize diverse negation queries, and introduce a dynamic visualfocus module for verification. Our evaluation reveals that advanced RSMLLMs struggle with negation, exhibiting hallucinations and substantialperformance degradation. To close this gap, we propose NeFo, a noveltest-time adaptation method that explicitly incorporates the logical roleof negation into the model optimization. Remarkably, using about 5%unlabeled test samples, NeFo significantly improves the negation under-standing of models and shows strong generalization to unseen tasks.