EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human Actions
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
Determining the veracity of atomic claims is an imperative component of many recently proposed fact-checking systems. Many approaches tackle this problem by first retrieving evidence by querying a search engine and then performing classification by providing the evidence set and atomic claim to a large language model, but this process deviates from what a human would do in order to perform the task. Recent work attempted to address this issue by proposing iterative evidence retrieval, allowing for evidence to be collected several times and only when necessary. Continuing along this line of research, we propose a novel claim verification system, called EMULATE, which is designed to better emulate human actions through the use of a multi-agent framework where each agent performs a small part of the larger task, such as ranking search results according to predefined criteria or evaluating webpage content. Extensive experiments on several benchmarks show clear improvements over prior work, demonstrating the efficacy of our new multi-agent framework.
Key facts. Cited by 3 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 9 of its 12 references that are in the map. First posted on arXiv 22 May 2025.
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9 of 12 references.
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Other references · 8
- 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
- 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
- 2024VERISCORE: Evaluating the factuality of verifiable claims in long-form text generationcited by 117
- 2023Complex Claim Verification with Evidence Retrieved in the Wildcited by 110
- 2023Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkerscited by 87
- 2024FIRE: Fact-checking with Iterative Retrieval and Verificationcited by 39
- 2024Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance?cited by 36
- 2024Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMscited by 24
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Exploring Health Misinformation Detection with Multi-Agent Debatecited by 0
- 2026TRUST Agents: A Collaborative Multi-Agent Framework for Fake News Detection, Explainable Verification, and Logic-Aware Claim Reasoningcited by 0
- 2026Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerabilitycited by 0
- 2026Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability11 Jul 2026
- 2026TRUST Agents: A Collaborative Multi-Agent Framework for Fake News Detection, Explainable Verification, and Logic-Aware Claim Reasoning14 Apr 2026
- 2025Exploring Health Misinformation Detection with Multi-Agent Debate29 Nov 2025
Similar, not cited
Close in wording, no citation either way
- 2025Towards Robust Fact-Checking: A Multi-Agent System with Advanced Evidence Retrieval
- 2026MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessment
- 2025Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification
- 2025RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking
- 2025Althea: The Fact-Checking--Metalearning Tradeoff in AI-Assisted Verification
- 2026EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy
- 2026ReflectFact: Self-Reflective Agents for Improving Comprehension and Reasoning in Multi-Hop Fact Verification
- 2025Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification