MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessment

2026-01-29

AI Summary

This work addresses a key limitation in existing fact-checking methods, which treat evidence retrieval as a static and isolated step, thereby hindering cross-claim evidence reuse and compromising both efficiency and consistency. To overcome this, the authors propose MERMAID, a novel framework that introduces a persistent evidence memory mechanism for the first time. MERMAID tightly couples retrieval and reasoning within an iterative reasoning-action loop through multi-agent collaboration and leverages structured knowledge representations to enable dynamic acquisition and reuse of evidence. The approach significantly enhances verification efficiency, consistency, and accuracy, achieving state-of-the-art performance across three fact-checking benchmarks and two claim verification datasets while effectively reducing redundant search operations.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Assessing the veracity of online content has become increasingly critical. Large language models (LLMs) have recently enabled substantial progress in automated veracity assessment, including automated fact-checking and claim verification systems. Typical veracity assessment pipelines break down complex claims into sub-claims, retrieve external evidence, and then apply LLM reasoning to assess veracity. However, existing methods often treat evidence retrieval as a static, isolated step and do not effectively manage or reuse retrieved evidence across claims. In this work, we propose MERMAID, a memory-enhanced multi-agent veracity assessment framework that tightly couples the retrieval and reasoning processes. MERMAID integrates agent-driven search, structured knowledge representations, and a persistent memory module within a Reason-Action style iterative process, enabling dynamic evidence acquisition and cross-claim evidence reuse. By retaining retrieved evidence in an evidence memory, the framework reduces redundant searches and improves verification efficiency and consistency. We evaluate MERMAID on three fact-checking benchmarks and two claim-verification datasets using multiple LLMs, including GPT, LLaMA, and Qwen families. Experimental results show that MERMAID achieves state-of-the-art performance while improving the search efficiency, demonstrating the effectiveness of synergizing retrieval, reasoning, and memory for reliable veracity assessment.

Problem

• veracity assessment
• evidence retrieval
• claim verification
• fact-checking
• knowledge reuse

Innovation

• memory-enhanced retrieval
• multi-agent reasoning
• evidence reuse
• iterative knowledge grounding
• veracity assessment