Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs

• URL: http://arxiv.org/abs/2606.20177v2
• Date: Mon, 22 Jun 2026 09:23:26 GMT
• Title: Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs
• Abstract summary: We introduce RS-Neg, the first benchmark to evaluate negation understanding across region-level to scene-level tasks.<n>We propose NeFo, a novel test-time learning method that explicitly incorporates the logical role of negation into the model optimization.
• Score: 21.808192032202232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in various Remote Sensing (RS) tasks. However, their ability to comprehend negation remains underexplored, limiting deployment in real-world applications where models must explicitly identify what is false or absent, e.g., emergency responders need to locate non-flooded routes for evacuation. To comprehensively study this limitation, we introduce RS-Neg, the first benchmark to evaluate negation understanding across region-level to scene-level tasks. Specifically, we design an automated data generation pipeline for RS imagery, using LLMs to synthesize diverse negation queries, and introduce a dynamic visual focus module for verification. Our evaluation reveals that advanced RS MLLMs struggle with negation, exhibiting hallucinations and substantial performance degradation. To close this gap, we propose NeFo, a novel test-time learning method that explicitly incorporates the logical role of negation into the model optimization. Remarkably, using about 5\% unlabeled test samples, NeFo significantly improves the negation understanding of models and shows strong generalization to unseen tasks.

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