MMD-Thinker: Adaptive Multi-Dimensional Thinking for Multimodal Misinformation Detection

• URL: http://arxiv.org/abs/2511.13242v1
• Date: Mon, 17 Nov 2025 11:04:30 GMT
• Title: MMD-Thinker: Adaptive Multi-Dimensional Thinking for Multimodal Misinformation Detection
• Authors: Junjie Wu, Guohong Fu,
• Abstract summary: Multimodal misinformation floods on various social media, and continues to evolve in the era of AI-generated content (AIGC)<n>Recent studies leverage general-purpose multimodal large language models (MLLMs) to achieve remarkable results in detection.<n>We propose MMD-Thinker, a two-stage framework for multimodal misinformation detection through adaptive multi-dimensional thinking.
• Score: 8.06079393106578
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal misinformation floods on various social media, and continues to evolve in the era of AI-generated content (AIGC). The emerged misinformation with low creation cost and high deception poses significant threats to society. While recent studies leverage general-purpose multimodal large language models (MLLMs) to achieve remarkable results in detection, they encounter two critical limitations: (1) Insufficient reasoning, where general-purpose MLLMs often follow the uniform reasoning paradigm but generate inaccurate explanations and judgments, due to the lack of the task-specific knowledge of multimodal misinformation detection. (2) Reasoning biases, where a single thinking mode make detectors a suboptimal path for judgment, struggling to keep pace with the fast-growing and intricate multimodal misinformation. In this paper, we propose MMD-Thinker, a two-stage framework for multimodal misinformation detection through adaptive multi-dimensional thinking. First, we develop tailor-designed thinking mode for multimodal misinformation detection. Second, we adopt task-specific instruction tuning to inject the tailored thinking mode into general-purpose MLLMs. Third, we further leverage reinforcement learning strategy with a mixed advantage function, which incentivizes the reasoning capabilities in trajectories. Furthermore, we construct the multimodal misinformation reasoning (MMR) dataset, encompasses more than 8K image-text pairs with both reasoning processes and classification labels, to make progress in the relam of multimodal misinformation detection. Experimental results demonstrate that our proposed MMD-Thinker achieves state-of-the-art performance on both in-domain and out-of-domain benchmark datasets, while maintaining flexible inference and token usage. Code will be publicly available at Github.

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