ArXiv

AutoSynthesis: An agentic system for automated meta-analysis

2026-07-16 · 17:45 UTC ·Moein Taherinezhad, Sebastian Maier, Gerardo Vitagliano... ·1 min read
Authors
Moein Taherinezhad, Sebastian Maier, Gerardo Vitagliano...
Categories
cs.AI
arXiv
https://arxiv.org/abs/2607.15247v1
PDF
https://arxiv.org/pdf/2607.15247v1

Brief

AutoSynthesis is a 2026 multi-agent pipeline that automates quantitative meta-analysis end-to-end: it builds search strategies, retrieves and screens studies, extracts numeric results, computes standardized effect sizes, and runs random-effects meta-analysis while supporting heterogeneity and bias assessment. In one application it screened >28 studies and extracted >20 claims; pooled estimates aligned with Hedges' g from expert meta-analyses. Summary based on the abstract (full text not reviewed).

Why it matters

AutoSynthesis is an end-to-end multi-agent system that, given a natural-language research question, automates search-strategy formulation, literature retrieval, screening, full-text eligibility assessment, quantitative-statistic extraction, standardized effect-size computation, and random-effects meta-analysis.

Key details

  • In the reported application AutoSynthesis screened over 28 studies and extracted more than 20 quantitative claims; its pooled effect estimates closely match Hedges' g from expert-conducted meta-analyses, indicating close agreement with manual evidence synthesis.
  • The system also supports heterogeneity analysis, risk-of-bias assessment, and produces transparent, PRISMA-aligned reports to improve scalability of quantitative evidence synthesis.
Source evidence

Abstract

Evidence synthesis is crucial for turning primary research into reliable knowledge for science, medicine, education, and policy. Yet, quantitative evidence synthesis remains largely manual and difficult to scale. Here, we introduce AutoSynthesis, an end-to-end multi-agent system for automated meta-analysis. Given a research question in natural language, AutoSynthesis formulates a search strategy, retrieves scientific literature, screens candidate studies, assesses full-text eligibility, extracts quantitative statistics, computes standardized effect sizes, and finally performs random-effects meta-analysis. AutoSynthesis further supports heterogeneity analysis to examine how effect sizes vary across moderators, as well as risk-of-bias assessment. As output, AutoSynthesis produces a transparent report aligned with PRISMA guidelines. In our application, AutoSynthesis screened over 28 studies and extracted more than 20 quantitative claims. The pooled effect estimates produced by AutoSynthesis are similar to Hedges' $g$ of expert-conducted meta-analyses, indicating close agreement with manual evidence synthesis. Together, these results show that AutoSynthesis can make quantitative evidence synthesis more scalable, thereby supporting evidence-based decision-making across disciplines.