arXiv:2604.05018cs.AI
PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing
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Abstract
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 submission-ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWritingBench, the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers, alongside a comprehensive suite of automated evaluators. In side-by-side human evaluations, PaperOrchestra significantly outperforms autonomous baselines, achieving an absolute win rate margin of 50%-68% in literature review quality, and 14%-38% in overall manuscript quality.
Google recently released PaperOrchestra (arXiv:2604.05018), a multi-agent framework that converts unstructured research materials, such as logs, ideas, and results, into submission-ready LaTeX manuscripts. It employs a specialized 5-aagents pipeline: Outline, Plotting/Lit…
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