VILLAIN at AVerImaTeC: Verifying Image-Text Claims via Multi-Agent Collaboration
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
This paper describes VILLAIN, a multimodal fact-checking system that verifies image-text claims through prompt-based multi-agent collaboration. For the AVerImaTeC shared task, VILLAIN employs vision-language model agents across multiple stages of fact-checking. Textual and visual evidence is retrieved from the knowledge store enriched through additional web collection. To identify key information and address inconsistencies among evidence items, modality-specific and cross-modal agents generate analysis reports. In the subsequent stage, question-answer pairs are produced based on these reports. Finally, the Verdict Prediction agent produces the verification outcome based on the image-text claim and the generated question-answer pairs. Our system ranked first on the leaderboard across all evaluation metrics. The source code is publicly available at https://github.com/ssu-humane/VILLAIN.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 14 references that are in the map. First posted on arXiv 4 Feb 2026.
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References · 8
- 2025Small Language Models are the Future of Agentic AIcited by 270
- 2023AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Webcited by 144
- 2024The Automated Verification of Textual Claims (AVeriTeC) Shared Taskcited by 29
- 2024Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checkingcited by 18
- 2025AVerImaTeC: A Dataset for Automatic Verification of Image-Text Claims with Evidence from the Webcited by 18
- 2024HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claimscited by 12
- 2025Team HUMANE at AVeriTeC 2025: HerO 2 for Efficient Fact Verificationcited by 4
- 2025AIC CTU@FEVER 8: On-premise fact checking through long context RAGcited by 3
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- 2026TRUST Agents: A Collaborative Multi-Agent Framework for Fake News Detection, Explainable Verification, and Logic-Aware Claim Reasoning
- 2026MIC: Explaining Image-Claim Inconsistencies in AI-Generated Multimodal Misinformation
- 2026RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild
- 2026LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger
- 2024MAD-Sherlock: Multi-Agent Debate for Visual Misinformation Detection
- 2026InSight: A Benchmark for Agentic Claim Verification in Interactive Visualizations