RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild

• URL: http://arxiv.org/abs/2605.10357v2
• Date: Tue, 12 May 2026 03:18:40 GMT
• Title: RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild
• Abstract summary: RW-Post is a post-aligned textbftext--image benchmark for real-world multimodal fact-checking.<n> RW-Post supports controlled evaluation across closed-book, evidence-bounded, and open-web regimes.
• Score: 9.55806677152407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal misinformation increasingly leverages visual persuasion, where repurposed or manipulated images strengthen misleading text. We introduce \textbf{RW-Post}, a post-aligned \textbf{text--image benchmark} for real-world multimodal fact-checking with \emph{auditable} annotations: each instance links the original social-media post with reasoning traces and explicitly linked evidence items derived from human fact-check articles via an LLM-assisted extraction-and-auditing pipeline. RW-Post supports controlled evaluation across closed-book, evidence-bounded, and open-web regimes, enabling systematic diagnosis of visual grounding and evidence utilization. We provide \textbf{AgentFact} as a reference verification baseline and benchmark strong open-source LVLMs under unified protocols. Experiments show substantial headroom: current models struggle with faithful evidence grounding, while evidence-bounded evaluation improves both accuracy and faithfulness. Code and dataset will be released at https://github.com/xudanni0927/AgentFact.

Related papers

• REVEAL: Reference-Grounded Reasoning for Multimodal Manipulation Detection [33.33464433821003]
  Multimodal manipulation detection aims to simultaneously identify forged image--text pairs and localize tampered regions.<n>Inspired by human comparative reasoning, we reformulate this task as a reference-grounded verification problem.<n>We propose REVEAL, a framework explicitly designed for this comparative paradigm.
  arXiv  Detail & Related papers  (2026-05-27T13:24:41Z)
• Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation [51.55755193937205]
  We propose QREAM, a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts.<n>Our framework consists of two stages: (1) QREAM-ICL, which uses stylistic seeds to guide iterative rewriting exploration; and (2) QREAM-FT, a lightweight student model distilled from denoised ICL outputs.
  arXiv  Detail & Related papers  (2026-04-19T08:39:21Z)
• Is a Picture Worth a Thousand Words? Adaptive Multimodal Fact-Checking with Visual Evidence Necessity [7.793738133162104]
  AMuFC is a multimodal fact-checking framework that employs two collaborative agents for the adaptive use of visual evidence.<n>We show that incorporating the Analyzer's assessment of visual evidence necessity into the Verifier's prediction yields substantial improvements in verification performance.<n>In addition to all code, we release WebFC, a newly constructed dataset for evaluating fact-checking modules in a more realistic scenario.
  arXiv  Detail & Related papers  (2026-04-06T14:01:38Z)
• FactReview: Evidence-Grounded Reviews with Literature Positioning and Execution-Based Claim Verification [57.196748998757954]
  We present FactReview, an evidence-grounded reviewing system that combines claim extraction, literature positioning, and execution-based claim verification.<n>Given a submission, FactReview identifies major claims and reported results, retrieves nearby work to clarify the paper's technical position, and, when code is available, executes the released repository.<n>It then produces a concise review and an evidence report that assigns each major claim one of five labels: Supported, Supported by the paper, Partially supported, In conflict, or Inconclusive.
  arXiv  Detail & Related papers  (2026-04-05T11:45:22Z)
• Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking [47.47518672198846]
  Misinformation spreading over the Internet poses a significant threat to both societies and individuals.<n>Previous methods rely on semantic and social-contextual patterns learned from training data.<n>We propose WKGFC, which exploits authorized open knowledge graph as a core resource of evidence.
  arXiv  Detail & Related papers  (2026-02-27T19:29:01Z)
• CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era [51.63024682584688]
  Large language models (LLMs) introduce a new risk: fabricated references that appear plausible but correspond to no real publications.<n>We present the first comprehensive benchmark and detection framework for hallucinated citations in scientific writing.<n>Our framework significantly outperforms prior methods in both accuracy and interpretability.
  arXiv  Detail & Related papers  (2026-02-26T19:17:39Z)
• D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detection [9.049034101566642]
  Multimodal misinformation increasingly mixes realistic im-age edits with fluent but misleading text, producing persuasive posts that are difficult to verify.<n>We present D-SECURE, a framework that combines internal manipulation detection with external evidence-based reasoning for news-style posts.
  arXiv  Detail & Related papers  (2026-02-16T03:51:49Z)
• Look As You Think: Unifying Reasoning and Visual Evidence Attribution for Verifiable Document RAG via Reinforcement Learning [55.232400251303794]
  Look As You Think (LAT) is a reinforcement learning framework that trains models to produce verifiable reasoning paths with consistent attribution.<n>LAT consistently improves the vanilla model in both single- and multi-image settings, yielding average gains of 8.23% in soft exact match (EM) and 47.0% in IoU@0.5.
  arXiv  Detail & Related papers  (2025-11-15T02:50:23Z)
• Unstructured Evidence Attribution for Long Context Query Focused Summarization [53.08341620504465]
  We propose to extract unstructured (i.e., spans of any length) evidence in order to acquire more relevant and consistent evidence than in the fixed granularity case.<n>We show how existing systems struggle to copy and properly cite unstructured evidence, which also tends to be "lost-in-the-middle"
  arXiv  Detail & Related papers  (2025-02-20T09:57:42Z)
• DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts [35.952854524873246]
  Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME) is a zero-shot MLLM pipeline for open-domain, text-image claim verification.<n>DEFAME operates in a six-stage process, dynamically selecting the tools and search depth to extract and evaluate textual and visual evidence.
  arXiv  Detail & Related papers  (2024-12-13T19:11:18Z)
• Multimodal Misinformation Detection using Large Vision-Language Models [7.505532091249881]
  Large language models (LLMs) have shown remarkable performance in various tasks. Few approaches consider evidence retrieval as part of misinformation detection. We propose a novel re-ranking approach for multimodal evidence retrieval.
  arXiv  Detail & Related papers  (2024-07-19T13:57:11Z)
• Give Me More Details: Improving Fact-Checking with Latent Retrieval [58.706972228039604]
  Evidence plays a crucial role in automated fact-checking. Existing fact-checking systems either assume the evidence sentences are given or use the search snippets returned by the search engine. We propose to incorporate full text from source documents as evidence and introduce two enriched datasets.
  arXiv  Detail & Related papers  (2023-05-25T15:01:19Z)

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.