RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking

• URL: http://arxiv.org/abs/2507.09174v1
• Date: Sat, 12 Jul 2025 07:46:51 GMT
• Title: RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking
• Authors: Shuo Yang, Zijian Yu, Zhenzhe Ying, Yuqin Dai, Guoqing Wang, Jun Lan, Jinfeng Xu, Jinze Li, Edith C. H. Ngai,
• Abstract summary: RAMA is a novel retrieval-augmented multi-agent framework designed for verifying multimedia misinformation.<n> RAMA incorporates three core innovations: (1) strategic query formulation that transforms multimodal claims into precise web search queries; (2) cross-verification evidence aggregation from diverse, authoritative sources; and (3) a multi-agent ensemble architecture.
• Score: 15.160356035522609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid proliferation of multimodal misinformation presents significant challenges for automated fact-checking systems, especially when claims are ambiguous or lack sufficient context. We introduce RAMA, a novel retrieval-augmented multi-agent framework designed for verifying multimedia misinformation. RAMA incorporates three core innovations: (1) strategic query formulation that transforms multimodal claims into precise web search queries; (2) cross-verification evidence aggregation from diverse, authoritative sources; and (3) a multi-agent ensemble architecture that leverages the complementary strengths of multiple multimodal large language models and prompt variants. Extensive experiments demonstrate that RAMA achieves superior performance on benchmark datasets, particularly excelling in resolving ambiguous or improbable claims by grounding verification in retrieved factual evidence. Our findings underscore the necessity of integrating web-based evidence and multi-agent reasoning for trustworthy multimedia verification, paving the way for more reliable and scalable fact-checking solutions. RAMA will be publicly available at https://github.com/kalendsyang/RAMA.git.

Related papers

• Multimodal Fact-Checking: An Agent-based Approach [9.55806677152407]
  We introduce RW-Post, a high-quality and explainable dataset for real-world multimodal fact-checking.<n> RW-Post aligns real-world multimodal claims with their original social media posts, preserving the rich contextual information in which the claims are made.<n>Building upon RW-Post, we propose AgentFact, an agent-based multimodal fact-checking framework designed to emulate the human verification workflow.
  arXiv  Detail & Related papers  (2025-12-28T13:58:33Z)
• MMD-Thinker: Adaptive Multi-Dimensional Thinking for Multimodal Misinformation Detection [8.06079393106578]
  Multimodal misinformation floods on various social media, and continues to evolve in the era of AI-generated content (AIGC)<n>Recent studies leverage general-purpose multimodal large language models (MLLMs) to achieve remarkable results in detection.<n>We propose MMD-Thinker, a two-stage framework for multimodal misinformation detection through adaptive multi-dimensional thinking.
  arXiv  Detail & Related papers  (2025-11-17T11:04:30Z)
• MAVIS: A Benchmark for Multimodal Source Attribution in Long-form Visual Question Answering [44.41273615523289]
  We introduce MAVIS, the first benchmark designed to evaluate multimodal source attribution systems.<n>Our dataset comprises 157K visual QA instances, where each answer is annotated with fact-level citations referring to multimodal documents.<n>We develop fine-grained automatic metrics along three dimensions of informativeness, groundedness, and fluency, and demonstrate their strong correlation with human judgments.
  arXiv  Detail & Related papers  (2025-11-15T10:14:59Z)
• UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG [82.84014669683863]
  Multimodal retrieval-augmented generation (MM-RAG) is a key approach for applying large language models to real-world knowledge bases.<n>UniDoc-Bench is the first large-scale, realistic benchmark for MM-RAG built from 70k real-world PDF pages.<n>Our experiments show that multimodal text-image fusion RAG systems consistently outperform both unimodal and jointly multimodal embedding-based retrieval.
  arXiv  Detail & Related papers  (2025-10-04T04:30:13Z)
• MR$^2$-Bench: Going Beyond Matching to Reasoning in Multimodal Retrieval [86.35779264575154]
  Multimodal retrieval is becoming a crucial component of modern AI applications, yet its evaluation lags behind the demands of more realistic and challenging scenarios.<n>We introduce MR$2$-Bench, a reasoning-intensive benchmark for multimodal retrieval.
  arXiv  Detail & Related papers  (2025-09-30T15:09:14Z)
• MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents [78.3863007028688]
  MM-BrowseComp is a novel benchmark comprising 224 challenging, hand-crafted questions.<n>These questions often incorporate images in prompts, and crucial information encountered during the search and reasoning process may also be embedded within images or videos on webpages.<n>Our comprehensive evaluation of state-of-the-art models on MM-BrowseComp reveals that even top models like OpenAI o3 with tools achieve only 29.02% accuracy.
  arXiv  Detail & Related papers  (2025-08-14T13:46:47Z)
• Multi-Stage Verification-Centric Framework for Mitigating Hallucination in Multi-Modal RAG [3.9063541371093184]
  This paper presents the technical solution developed by team CRUISE for the KDD Cup 2025 Meta Comprehensive RAG Benchmark for Multi-modal, Multi-turn (CRAG-MM)<n>The challenge aims to address a critical limitation of modern Vision Language Models (VLMs): their propensity to hallucinate.<n>Our solution integrates a lightweight query router for efficiency, a query-aware retrieval and summarization pipeline, a dual-pathways generation and a post-hoc verification.
  arXiv  Detail & Related papers  (2025-07-27T05:45:45Z)
• Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective [42.832839189236694]
  We propose MAMMQA, a multi-agent QA framework for multimodal inputs spanning text, tables, and images.<n>Our system includes two Visual Language Model (VLM) agents and one text-based Large Language Model (LLM) agent.<n> Experiments on diverse multimodal QA benchmarks demonstrate that our cooperative, multi-agent framework consistently outperforms existing baselines in both accuracy and robustness.
  arXiv  Detail & Related papers  (2025-05-27T07:23:38Z)
• T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search [51.91311158085973]
  multimodal misinformation often arises from mixed forgery sources, requiring dynamic reasoning and adaptive verification.<n>We propose T2Agent, a novel misinformation detection agent that incorporates a toolkit with Monte Carlo Tree Search.<n>Extensive experiments show that T2Agent consistently outperforms existing baselines on challenging mixed-source multimodal misinformation benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T09:50:55Z)
• Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering [42.468210353582755]
  Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents.<n>Current document retrieval-augmented generation (DocRAG) methods remain limited by their text-centric approaches.<n>We introduce MMDocRAG, a comprehensive benchmark featuring 4,055 expert-annotated QA pairs with multi-page, cross-modal evidence chains.
  arXiv  Detail & Related papers  (2025-05-22T09:52:57Z)
• MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration [63.31211701741323]
  We extend multi-agent multi-model reasoning to generation, specifically to improving faithfulness through refinement.<n>We design intrinsic evaluations for each subtask, with our findings indicating that both multi-agent (multiple instances) and multi-model (diverse LLM types) approaches benefit error detection and critiquing.<n>We consolidate these insights into a final "recipe" called Multi-Agent Multi-Model Refinement (MAMM-Refine), where multi-agent and multi-model collaboration significantly boosts performance.
  arXiv  Detail & Related papers  (2025-03-19T14:46:53Z)
• Knowledge-Aware Iterative Retrieval for Multi-Agent Systems [0.0]
  We introduce a novel large language model (LLM)-driven agent framework.<n>It iteratively refines queries and filters contextual evidence by leveraging dynamically evolving knowledge.<n>The proposed system supports both competitive and collaborative sharing of updated context.
  arXiv  Detail & Related papers  (2025-03-17T15:27:02Z)
• Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts [56.30364248231053]
  This paper introduces Multi-Modal Retrieval-Augmented Generation (M2RAG)<n>M2RAG is a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models (MLLMs)<n>To enhance the context utilization capabilities of MLLMs, we also introduce Multi-Modal Retrieval-Augmented Instruction Tuning (MM-RAIT)
  arXiv  Detail & Related papers  (2025-02-24T16:25:25Z)
• Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation [2.549112678136113]
  Retrieval-Augmented Generation (RAG) mitigates issues by integrating external dynamic information for improved factual grounding.<n>Cross-modal alignment and reasoning introduce unique challenges beyond those in unimodal RAG.<n>This survey lays the foundation for developing more capable and reliable AI systems.
  arXiv  Detail & Related papers  (2025-02-12T22:33:41Z)
• MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt [60.10555128510744]
  Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities.<n>Recently, large-scale pre-trained models like CLIP have demonstrated impressive performance in traditional single-modal object ReID tasks.<n>We introduce a novel framework called MambaPro for multi-modal object ReID.
  arXiv  Detail & Related papers  (2024-12-14T06:33:53Z)
• VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation [100.06122876025063]
  This paper introduces VisDoMBench, the first comprehensive benchmark designed to evaluate QA systems in multi-document settings.<n>We propose VisDoMRAG, a novel multimodal Retrieval Augmented Generation (RAG) approach that simultaneously utilizes visual and textual RAG.
  arXiv  Detail & Related papers  (2024-12-14T06:24:55Z)
• Multi-modal Retrieval Augmented Multi-modal Generation: Datasets, Evaluation Metrics and Strong Baselines [63.22096609916707]
  Multi-modal Retrieval Augmented Multi-modal Generation (M$2$RAG) is a novel task that enables foundation models to process multi-modal web content.<n>Despite its potential impact, M$2$RAG remains understudied, lacking comprehensive analysis and high-quality data resources.
  arXiv  Detail & Related papers  (2024-11-25T13:20:19Z)
• Robust Modality-incomplete Anomaly Detection: A Modality-instructive Framework with Benchmark [69.02666229531322]
  We introduce a pioneering study that investigates Modality-Incomplete Industrial Anomaly Detection (MIIAD)<n>We find that most existing MIAD methods perform poorly on the MIIAD Bench, leading to significant performance degradation.<n>We propose a novel two-stage Robust modAlity-aware fusing and Detecting framewoRk, abbreviated as RADAR.
  arXiv  Detail & Related papers  (2024-10-02T16:47:55Z)
• Detecting and Grounding Multi-Modal Media Manipulation and Beyond [93.08116982163804]
  We highlight a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM4) DGM4 aims to not only detect the authenticity of multi-modal media, but also ground the manipulated content. We propose a novel HierArchical Multi-modal Manipulation rEasoning tRansformer (HAMMER) to fully capture the fine-grained interaction between different modalities.
  arXiv  Detail & Related papers  (2023-09-25T15:05:46Z)

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.