mlsanityData to 14 Oct 2026

MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection

arXiv 2508.10143 ↗First posted 13 Aug 2025cs.AIcited by 4

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

The large spread of disinformation across digital platforms creates significant challenges to information integrity. This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles, focusing on titles and short text snippets. The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent (which relies on named entity recognition), (iii) a coherence detection agent (using LLM prompt engineering), and (iv) a web-scraped data analyzer that extracts relational triplets for fact checking. The system is orchestrated via the Model Context Protocol (MCP), offering shared context and live learning across components. Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches. The weighted aggregation method, mathematically derived from individual agent misclassification rates, proves superior to algorithmic threshold optimization. The modular architecture makes the system easily scalable, while also maintaining details of the decision processes.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 15 of its 45 references that are in the map. First posted on arXiv 13 Aug 2025.

Builds on

15 of 45 references.

References · 15

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
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  5. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
  6. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 265
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  8. 2020Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-makingcited by 59
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  10. 2018A review of possible effects of cognitive biases on the interpretation of rule-based machine learning modelscited by 22
  11. 2023Graph Neural Networks for Text Classification: A Surveycited by 15
  12. 2024Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherencecited by 7
3 more
  1. 2024Revisiting Fake News Detection: Towards Temporality-aware Evaluation by Leveraging Engagement Earlinesscited by 3
  2. 2019On the Coherence of Fake News Articlescited by 1
  3. 2025Knowledge Graph-Driven Retrieval-Augmented Generation: Integrating Deepseek-R1 with Weaviate for Advanced Chatbot Applicationscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Multi-Agentic System Leveraging Open-Source LLMs to Mitigate Disinformation Threatscited by 0
  2. 2026Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerabilitycited by 0
  3. 2026An Explainable Coherence Score for Detecting Temporal Inconsistencies in Political Newscited by 0
  4. 2026Memory-First Fact-Checking: A Knowledge-Graph-Grounded Multi-Agent System for Misinformation Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Multi-agent Systems for Misinformation Lifecycle : Detection, Correction And Source Identification
  2. 2026TRUST Agents: A Collaborative Multi-Agent Framework for Fake News Detection, Explainable Verification, and Logic-Aware Claim Reasoning
  3. 2025Toward Verifiable Misinformation Detection: A Multi-Tool LLM Agent Framework
  4. 2025Towards Robust Fact-Checking: A Multi-Agent System with Advanced Evidence Retrieval
  5. 2025RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking
  6. 2025The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News
  7. 2024Web Retrieval Agents for Evidence-Based Misinformation Detection
  8. 2026PAMAS: Self-Adaptive Multi-Agent System with Perspective Aggregation for Misinformation Detection