Evidence Triangulation for Multimodal Fact-Checking in the Wild

• URL: http://arxiv.org/abs/2606.31367v1
• Date: Tue, 30 Jun 2026 08:59:12 GMT
• Title: Evidence Triangulation for Multimodal Fact-Checking in the Wild
• Abstract summary: We introduce X-POSE, a benchmark of real-world, community-annotated multimodal posts from X (formerly Twitter)<n>We also propose TRENT, a novel MFC model that performs evidence triangulation using three parallel cross-attention streams.
• Score: 14.259306741814372
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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

Related papers

• POINTS-Seeker: Towards Training a Multimodal Agentic Search Model from Scratch [84.73366911912512]
  We introduce Agentic Seeding, a dedicated phase designed to weave the precursors necessary for eliciting agentic behaviors.<n>We propose V-Fold, an adaptive history-aware compression scheme that preserves recent dialogue turns in high fidelity while folding historical context into the visual space via rendering.<n>We develop POINTS-Seeker-8B, a state-of-the-art multimodal agentic search model that consistently outperforms existing models across six diverse benchmarks.
  arXiv  Detail & Related papers  (2026-04-15T16:09:37Z)
• Rethinking Multimodal Fusion for Time Series: Auxiliary Modalities Need Constrained Fusion [12.754011132549167]
  We show that multimodal models with naive fusion strategies often underperform unimodal TS models.<n>We propose constrained fusion methods designed to control such integration.<n>We conduct over 20K experiments across various datasets and TS/text models.
  arXiv  Detail & Related papers  (2026-03-23T07:44:46Z)
• NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching [64.10695425442164]
  We introduce NExT-OMNI, an open-source omnimodal foundation model that achieves unified modeling through discrete flow paradigms.<n>Trained on large-scale interleaved text, image, video, and audio data, NExT-OMNI delivers competitive performance on multimodal generation and understanding benchmarks.<n>To advance further research, we release training details, data protocols, and open-source both the code and model checkpoints.
  arXiv  Detail & Related papers  (2025-10-15T16:25:18Z)
• Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline [56.790045049514326]
  Two major forms of deception dominate: human-crafted misinformation and AI-generated content.<n>We propose Unified Multimodal Fake Content Detection (UMFDet), a framework designed to handle both forms of deception.<n>UMFDet achieves robust and consistent performance across both misinformation types, outperforming specialized baselines.
  arXiv  Detail & Related papers  (2025-09-30T09:26:32Z)
• Consistency-aware Fake Videos Detection on Short Video Platforms [4.291448222735821]
  This paper focuses on detecting fake news on the short video platforms.<n>Existing approaches typically combine raw video data with metadata inputs before applying a classification layer.<n>Motivated by this insight, we propose a novel detection paradigm that explicitly identifies and leverages cross-modal contradictions.
  arXiv  Detail & Related papers  (2025-04-30T10:26:04Z)
• Deconfounded Reasoning for Multimodal Fake News Detection via Causal Intervention [16.607714608483164]
  The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms.<n>Traditional unimodal detection methods fall short in addressing complex cross-modal manipulations.<n>We propose the Causal Intervention-based Multimodal Decon Detection framework.
  arXiv  Detail & Related papers  (2025-04-12T09:57:43Z)
• CoLLM: A Large Language Model for Composed Image Retrieval [76.29725148964368]
  Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query.<n>We present CoLLM, a one-stop framework that generates triplets on-the-fly from image-caption pairs.<n>We leverage Large Language Models (LLMs) to generate joint embeddings of reference images and modification texts.
  arXiv  Detail & Related papers  (2025-03-25T17:59:50Z)
• Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation [61.91492500828508]
  Few-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal support samples.<n>We introduce a multimodal FS-PCS setup, utilizing textual labels and the potentially available 2D image modality.<n>We propose a simple yet effective Test-time Adaptive Cross-modal (TACC) technique to mitigate training bias.
  arXiv  Detail & Related papers  (2024-10-29T19:28:41Z)
• Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection? [15.66049149213069]
  Out-of-context (OOC) misinformation poses a significant challenge in multimodal fact-checking. Recent research in evidence-based OOC detection has seen a trend towards increasingly complex architectures. We introduce a simple yet robust baseline, which assesses similarity between image-text pairs and external image and text evidence.
  arXiv  Detail & Related papers  (2024-07-18T13:08:55Z)
• Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion [54.33764537135906]
  VideoQA Transformer models demonstrate competitive performance on standard benchmarks. Do these models capture the rich multimodal structures and dynamics from video and text jointly? Are they achieving high scores by exploiting biases and spurious features?
  arXiv  Detail & Related papers  (2023-06-15T06:45:46Z)
• Continual Multimodal Knowledge Graph Construction [62.77243705682985]
  Current Multimodal Knowledge Graph Construction (MKGC) models struggle with the real-world dynamism of continuously emerging entities and relations. This study introduces benchmarks aimed at fostering the development of the continual MKGC domain. We introduce MSPT framework, designed to surmount the shortcomings of existing MKGC approaches during multimedia data processing.
  arXiv  Detail & Related papers  (2023-05-15T14:58:28Z)
• VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias [17.107961913114778]
  multimodal misinformation is a growing problem on social media platforms. In this study, we investigate and identify the presence of unimodal bias in widely-used MMD benchmarks. We introduce a new method -- termed Crossmodal HArd Synthetic MisAlignment (CHASMA) -- for generating realistic synthetic training data.
  arXiv  Detail & Related papers  (2023-04-27T12:28:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.