PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing

Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi-agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre-writing materials into publication-ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWriting-Eval200 , the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers, alongside a suite of automated peer-review evaluators. PaperOrchestra significantly improves literature review depth and citation coverage, outperforming autonomous baselines with 90%–99% win rates and reaching human-level parity. It also outperforms autonomous baselines on overall manuscript quality by 14%–38%, as measured by side-by-side human evaluation.

• Machine intelligence