Problem
Research questions and friction points this paper is trying to address.
automated paper writing
literature synthesis
unstructured research materials
scientific manuscript generation
AI-driven scientific discovery
PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing
🤖 AI Summary
This work addresses the limitations of existing approaches in automatically transforming unstructured scientific materials into high-quality academic papers—particularly their tight coupling and superficial literature reviews—by introducing the first end-to-end multi-agent paper generation framework. The proposed system leverages a collaborative multi-agent architecture, semantic synthesis of scholarly references, and a visualization generation model to flexibly process arbitrary pre-writing inputs and produce complete LaTeX manuscripts featuring in-depth literature reviews and automatically generated figures and tables. To facilitate systematic evaluation, the study also establishes PaperWritingBench, the first standardized benchmark for academic paper generation, alongside an automated assessment protocol. Human blind evaluations demonstrate that the method achieves a 50%–68% improvement in win rate for literature review quality and a 14%–38% gain in overall paper quality compared to prior approaches.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-agent framework
automated paper writing
literature synthesis
scientific visualization
PaperWritingBench
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