# Multimedia Verification Through Multi-Agent Deep Research Multimodal Large Language Models

arXiv: https://arxiv.org/abs/2507.04410 · HTML: https://mlsanity.com/paper/2507.04410/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in MLLM jailbreaks and adversarial robustness, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 6 Jul 2025.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM jailbreaks and adversarial robustness](https://mlsanity.com/mllm/mllm-jailbreaks-and-adversarial-robustness/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 11 of 11)

- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference Optimization](https://mlsanity.com/paper/2411.02712/) (2024)
- [Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate](https://mlsanity.com/paper/2402.07401/) (2024)
- [DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts](https://mlsanity.com/paper/2412.10510/) (2024)
- [FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs](https://mlsanity.com/paper/2403.01988/) (2024)
- [Logical Consistency of Large Language Models in Fact-checking](https://mlsanity.com/paper/2412.16100/) (2024)
- [LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks](https://mlsanity.com/paper/2402.12525/) (2024)
- [Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitors](https://mlsanity.com/paper/2505.11612/) (2025)
- [Evidence-Grounded Multimodal Misinformation Detection with Attention-Based GNNs](https://mlsanity.com/paper/2505.18221/) (2025)
- [PicHunt: Social Media Image Retrieval for Improved Law Enforcement](https://mlsanity.com/paper/1608.00905/) (2016)
- [E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs](https://mlsanity.com/paper/2506.20944/) (2025)

## Built on by (most cited first, 4 of 4)

- [Contestable Multi-Agent Debate with Arena-based Argumentative Computation for Multimedia Verification](https://mlsanity.com/paper/2605.14495/) (2026)
- [Consensus-based Agentic Large Language Model Framework for Harmonized Tariff Schedule Code Classification](https://mlsanity.com/paper/2606.16987/) (2026)
- [TUE-Detector: A Tool-Using Expert MLLM-Based Detector for AI-Generated Videos](https://mlsanity.com/paper/2608.30704/) (2026)
- [Self-Evolving Multimedia Verification through Memory Consolidation of Contestation Experiences](https://mlsanity.com/paper/2609.27175/) (2026)

## Abstract (arXiv)

This paper presents our submission to the ACMMM25 - Grand Challenge on Multimedia Verification. We developed a multi-agent verification system that combines Multimodal Large Language Models (MLLMs) with specialized verification tools to detect multimedia misinformation. Our system operates through six stages: raw data processing, planning, information extraction, deep research, evidence collection, and report generation. The core Deep Researcher Agent employs four tools: reverse image search, metadata analysis, fact-checking databases, and verified news processing that extracts spatial, temporal, attribution, and motivational context. We demonstrate our approach on a challenge dataset sample involving complex multimedia content. Our system successfully verified content authenticity, extracted precise geolocation and timing information, and traced source attribution across multiple platforms, effectively addressing real-world multimedia verification scenarios.