ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection
Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed provenance, and subtle cross-modal framing errors. Existing benchmarks and methods remain poorly matched to this setting: they usually isolate short captions, single images, binary labels, or one manipulation source, while agentic verification remains costly under realistic evidence search. We present ReMMD, a realistic multilingual multi-image agentic verification framework for multimodal misinformation detection. ReMMD includes ReMMDBench, a real-world multimodal misinformation detection benchmark with 500 samples, 2,756 images, five monolingu...
| Authors | Chenhao Dang, Dantong Zhu, Jun Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 June 2026 |
| Last updated at source | 11 August 2026 |
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- DDAI-D/ReMMDBenchDataset · cited by
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- DDAI-D/ReMMDBenchDataset
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