---
dataset_info:
  features:
  - name: field
    dtype: string
  - name: paper_idx
    dtype: string
  - name: doi
    dtype: string
  - name: type
    dtype: string
  - name: table_or_image
    dtype: image
  - name: text_or_caption
    dtype: string
  splits:
  - name: atmosphere
    num_bytes: 202134712.5
    num_examples: 1196
  - name: agriculture
    num_bytes: 446617002
    num_examples: 4336
  - name: environment
    num_bytes: 165016111.375
    num_examples: 1125
  download_size: 779035060
  dataset_size: 813767825.875
configs:
- config_name: default
  data_files:
  - split: atmosphere
    path: data/atmosphere-*
  - split: agriculture
    path: data/agriculture-*
  - split: environment
    path: data/environment-*
license: mit
language:
- en
---

# Manalyzer: End-to-end Automated Meta-analysis with Multi-agent System

## Overview

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.

## Paper

https://arxiv.org/pdf/2505.20310

## Project Page

https://black-yt.github.io/meta-analysis-page/

## GitHub Code

https://github.com/black-yt/Manalyzer