Paper Detail
Chenhao Dang, Dantong Zhu, Jun Yang, Conghui He, Weijia Li
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.
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@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},
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},
url = {https://huggingface.co/papers/2606.24112},
keywords = {multimodal misinformation detection, agentic verification, ReMMDBench, ReMMD-Agent, LVLMs, evidence provenance, veracity labels, distortion labels, structured L1/L2/L3 outputs, GPT-5.2, code available, huggingface daily},
eprint = {2606.24112},
archiveprefix = {arXiv},
}
{}