MERMAID：基于多智能体迭代知识接地的记忆增强检索与推理用于真实性评估
MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessment
AI总结：
MERMAID通过整合智能体驱动搜索、结构化知识表示和持久记忆模块，提升事实核查和主张验证的效率与一致性，实现检索、推理与记忆的协同优化。
摘要：
评估在线内容的真实性已成为越来越关键的任务。大型语言模型（LLMs）最近在自动化真实性评估中取得了显著进展，包括自动事实核查和主张验证系统。典型的真相评估流程将复杂主张分解为子主张，检索外部证据，然后应用LLM推理来评估真实性。然而，现有方法往往将证据检索视为一个静态、孤立的步骤，并未有效管理或重用检索到的证据跨主张。在本工作中，我们提出MERMAID，一种记忆增强的多智能体真实性评估框架，紧密耦合检索和推理过程。MERMAID在Reason-Action风格的迭代过程中整合了智能体驱动搜索、结构化知识表示和持久记忆模块，实现了动态证据获取和跨主张证据重用。通过在证据记忆中保留检索到的证据，该框架减少了冗余搜索，提高了验证效率和一致性。我们使用多个LLM（包括GPT、LLaMA和Qwen系列）在三个事实核查基准和两个主张验证数据集上评估MERMAID。实验结果表明，MERMAID在达到最新水平性能的同时，提高了搜索效率，证明了检索、推理和记忆协同优化在可靠真实性评估中的有效性。
英文摘要：
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.
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