This article introduces PaperOrchestra, a multi-agent system that transforms raw idea summaries and experimental logs into submission-ready AI research papers using agents for outlining, plotting, literature review, writing, and refinement. It outperforms single-agent and state-of-the-art baselines on PaperWritingBench, a new benchmark of 200 CVPR and ICLR papers, in both literature review and overall manuscript quality.

• PaperOrchestra uses a multi-agent pipeline (outline, plots, citation graph via Semantic Scholar, LaTeX writing, peer-review-style refinement) to turn raw idea summaries and experiment logs into full draft papers.
• Tested on a new 200-paper benchmark (100 CVPR 2025, 100 ICLR 2025), it beat a monolithic LLM pipeline and AI Scientist-v2 by 50–68 points on literature-review quality and 14–38 points on overall manuscript quality in blind human evaluations by 11 AI researchers.
• Grounding citations in real API data and using iterative self-reflection agents reduced hallucinated references and shallow reviews compared to baselines.
• The tool is positioned as a drafting accelerator, not a replacement for authors—humans must still verify facts, originality, and ethics despite built-in flags for dubious content.