Meta-analysis is a systematic research methodology that synthesizes data from multiple existing studies to derive comprehensive conclusions. This approach not only mitigates limitations inherent in individual studies but also facilitates novel discoveries through integrated data analysis. Traditional meta-analysis involves a complex multi-stage pipeline including literature retrieval, paper screening, and data extraction, which demands substantial human effort and time. However, while LLM-based methods can accelerate certain stages, they still face significant challenges, such as hallucinations in paper screening and data extraction. In this paper, we propose a multi-agent system, Manalyzer, which achieves end-to-end automated meta-analysis through tool calls. The hybrid review, hierarchical extraction, self-proving, and feedback checking strategies implemented in Manalyzer significantly alleviate these two hallucinations. To comprehensively evaluate the performance of meta-analysis, we construct a new benchmark comprising 729 papers across 3 domains, encompassing text, image, and table modalities, with over 10,000 data points. Extensive experiments demonstrate that Manalyzer achieves significant performance improvements over the LLM baseline in multi meta-analysis tasks. Project page: https://black-yt.github.io/meta-analysis-page/ .

翻译：元分析是一种系统性的研究方法，通过综合多个现有研究的数据来得出全面结论。该方法不仅能减轻单个研究固有的局限性，还能通过整合数据分析促进新发现。传统的元分析涉及复杂的多阶段流程，包括文献检索、论文筛选和数据提取，需要大量人力和时间。然而，尽管基于大语言模型的方法可以加速某些阶段，但仍面临重大挑战，例如论文筛选和数据提取中的幻觉问题。本文提出了一种多智能体系统Manalyzer，通过工具调用实现端到端的自动化元分析。Manalyzer中实现的混合审查、分层提取、自我验证和反馈检查策略显著缓解了这两种幻觉问题。为全面评估元分析性能，我们构建了一个包含3个领域（涵盖文本、图像和表格模态）共729篇论文的新基准数据集，包含超过10,000个数据点。大量实验表明，Manalyzer在多项元分析任务中相比大语言模型基线取得了显著的性能提升。项目页面：https://black-yt.github.io/meta-analysis-page/。