MERIT: Modular Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning

📰 ArXiv cs.AI

Learn how to detect multimodal misinformation using MERIT, a modular framework that achieves 81.65% F1 score, and apply its four specialized modules to improve misinformation detection

advanced Published 28 Apr 2026

Action Steps

Decompose misinformation detection into four modules: visual forensics, cross-modal alignment, retrieval-augmented claim verification, and calibrated judgment
Implement visual forensics using computer vision techniques to analyze image and video authenticity
Apply cross-modal alignment to ensure consistency between text and visual elements
Use retrieval-augmented claim verification to fact-check claims against web-grounded evidence
Calibrate judgment using machine learning models to predict the likelihood of misinformation

Who Needs to Know This

AI researchers and engineers working on misinformation detection can benefit from MERIT's modular framework to improve their models' performance and accuracy

Key Insight

💡 Modular frameworks can improve misinformation detection by decomposing the task into specialized modules

Key Takeaways

Learn how to detect multimodal misinformation using MERIT, a modular framework that achieves 81.65% F1 score, and apply its four specialized modules to improve misinformation detection

Full Article

Title: MERIT: Modular Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning

Abstract:
arXiv:2510.17590v2 Announce Type: replace Abstract: We present MERIT, an inference-time modular framework for multimodal misinformation detection that decomposes verification into four specialized modules: visual forensics, cross-modal alignment, retrieval-augmented claim verification, and calibrated judgment. On MMFakeBench, MERIT with GPT-4o-mini achieves 81.65% F1, outperforming all reported zero-shot baselines including GPT-4V with MMD-Agent (74.0% F1). A controlled same-model evaluation con

Read full paper → ← Back to Reads