PaperOrchestra tackles the gap between raw research materials and a polished conference submission. You feed it an idea summary, an experimental log with extracted numbers, and a LaTeX template. The system then splits the work across specialized agents: one drafts a structured outline, another generates plots and diagrams, a third builds a citation graph via Semantic Scholar API, a fourth writes each section in LaTeX, and a final agent refines the draft using simulated peer-review. To test its flexibility, the authors built PaperWritingBench from 200 top-tier AI papers (100 CVPR 2025, 100 ICLR 2025). That mix forces the system to handle both double-column CVPR layouts and single-column ICLR formats. Each benchmark item includes the core methodology summary, data tables turned into logs, and venue rules—no polished prose. This setup isolates writing from experimentation, reflecting real workflows where experiments are done but drafting isn’t. In human evaluations against two baselines—a monolithic LLM pipeline and AI Scientist-v2—PaperOrchestra won literature-review quality by 50–68 percentage points and overall manuscript quality by 14–38 points. Eleven AI researchers compared blind pairs: PaperOrchestra versus each baseline and versus ground truth. The system’s API-grounded citation checks and iterative self-reflection agents cut down on hallucinations and shallow reviews. The team emphasizes that PaperOrchestra speeds up drafting rather than replacing authors. Researchers must verify facts, origins, and ethics. Built-in safeguards flag dubious citations and content, but final responsibility for accuracy and originality stays with the human user.