| title | ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection |
|---|---|
| authors | Chenhao Dang, Dantong Zhu, Jun Yang, Conghui He, Weijia Li |
| date | 2026-06-23 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.24112 |
| affiliation | OpenDataLab |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed provenance, and subtle text--image 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 monolingual languages, two cross-lingual settings, three text-length tiers, multi-image posts, five-way veracity labels, eight distortion labels, evidence provenance, and rationales. It also includes ReMMD-Agent, a persistent-memory verifier that decomposes posts into atomic points, builds a reusable evidence set, and predicts structured L1/L2/L3 outputs. Across proprietary systems, open LVLMs, MMD-Agent, and T2-Agent, ReMMD-Agent obtains the best five-way veracity performance, with 41.80% accuracy and 39.12% macro-F1 using GPT-5.2, while reducing cost by 17.5% relative to MMD-Agent and 79.9% relative to T2-Agent. The project is available at https://dang-ai.github.io/ReMMD. |
cite
@misc{dang2026remmd,
title={ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection},
author={Chenhao Dang and Dantong Zhu and Jun Yang and Conghui He and Weijia Li},
year={2026},
eprint={2606.24112},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.24112}
}