{"ID":5438746,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T08:54:25.326461322Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31367","arxiv_id":"2606.31367","title":"Evidence Triangulation for Multimodal Fact-Checking in the Wild","abstract":"The proliferation of multimedia content on social platforms has fueled multimodal misinformation, where images are used to reinforce false claims. Consequently, Multimodal Fact-Checking (MFC) has emerged as an increasingly important research area. However, current progress is hindered by a reliance on synthetic training data and curated benchmarks that fail to capture the complexity of in-the-wild data. Furthermore, existing detection models rely on restricted intra-modality consistency or unconstrained all-to-all fusion, failing to capture nuanced relations between posts and external evidence. To address these limitations, we introduce X-POSE, a benchmark of real-world, community-annotated multimodal posts from X (formerly Twitter), augmented with full-length news articles retrieved via VLM-optimized search. Additionally, we propose TRENT, a novel MFC model that performs evidence triangulation using three parallel cross-attention streams alongside a relational fusion mechanism that explicitly models entailment and contradiction. Extensive evaluations demonstrate that TRENT consistently outperforms state-of-the-art specialized models and commercial VLMs. The code, prompt templates, and dataset are available at https://github.com/stevejpapad/evidence-triangulation","short_abstract":"The proliferation of multimedia content on social platforms has fueled multimodal misinformation, where images are used to reinforce false claims. Consequently, Multimodal Fact-Checking (MFC) has emerged as an increasingly important research area. However, current progress is hindered by a reliance on synthetic trainin...","url_abs":"https://arxiv.org/abs/2606.31367","url_pdf":"https://arxiv.org/pdf/2606.31367v1","authors":"[\"Stefanos-Iordanis Papadopoulos\",\"Zacharias Chrysidis\",\"Christos Koutlis\",\"Symeon Papadopoulos\",\"Panagiotis C. Petrantonakis\"]","published":"2026-06-30T08:59:12Z","proceeding":"cs.MM","tasks":"[\"cs.MM\",\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":613777,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-01T01:17:58.482524686Z","DeletedAt":null,"paper_id":5438746,"paper_url":"https://arxiv.org/abs/2606.31367","paper_title":"Evidence Triangulation for Multimodal Fact-Checking in the Wild","repo_url":"https://github.com/stevejpapad/evidence-triangulation","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}