Hallucination-aware Faithful Reasoning in Remote Sensing

Key points

• Multimodal large language models (MLLMs) have demonstrated promising capabilities for remote sensing reasoning, yet they frequently generate visually unsupported reasoning that contradicts the visual evidence.1
• Existing post-training methods optimize primarily for final-answer correctness, providing limited supervision for identifying and correcting hallucinations that arise during the reasoning process.1
• We first construct SFHD-43K, a dataset of 43K structured, fine-grained hallucination annotations, and train a detector to identify hallucination types, underlying causes, and supporting visual evidence.1
• Experiments with Qwen3.5 backbones (0.8B, 4B, and 9B) show consistent improvements across four remote sensing benchmarks, with gains of up to 11.64 points.1

Sources (1)

• [1]Hallucination-aware Faithful Reasoning in Remote Sensing
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 01:28 PM

  To address this limitation, we propose FaithRS, a hallucination-aware self-reflection framework for faithful reasoning in remote sensing.

  Multimodal large language models (MLLMs) have demonstrated promising capabilities for remote sensing reasoning, yet they frequently generate visually unsupported reasoning that contradicts the visual evidence.

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