Seeing Clearly without Training: Mitigating Hallucinations in Multimodal LLMs for Remote Sensing

• URL: http://arxiv.org/abs/2603.02754v1
• Date: Tue, 03 Mar 2026 08:53:20 GMT
• Title: Seeing Clearly without Training: Mitigating Hallucinations in Multimodal LLMs for Remote Sensing
• Abstract summary: Multimodal large language models (MLLMs) suffer from pronounced hallucinations in remote sensing visual question-answering (RS-VQA)<n>We introduce RSHBench, a protocol-based benchmark for fine-grained diagnosis of factual and logical hallucinations.<n>We propose Relative Attention-Driven Actively Reasoning (RADAR) to guide progressive localization and fine-grained local reasoning at test time.
• Score: 38.87853049845978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models (MLLMs) suffer from pronounced hallucinations in remote sensing visual question-answering (RS-VQA), primarily caused by visual grounding failures in large-scale scenes or misinterpretation of fine-grained small targets. To systematically analyze these issues, we introduce RSHBench, a protocol-based benchmark for fine-grained diagnosis of factual and logical hallucinations. To mitigate grounding-induced factual hallucinations, we further propose Relative Attention-Driven Actively Reasoning (RADAR), a training-free inference method that leverages intrinsic attention in MLLMs to guide progressive localization and fine-grained local reasoning at test time. Extensive experiments across diverse MLLMs demonstrate that RADAR consistently improves RS-VQA performance and reduces both factual and logical hallucinations. Code and data will be publicly available at: https://github.com/MiliLab/RADAR

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