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---
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:
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num_bytes: 202134712.5
num_examples: 1196
- name: agriculture
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- name: environment
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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 |