SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and
  Multi-dimensional Evaluation for Automated Survey Writing
Authors: name: Xiangchao Yan, name: Shiyang Feng, name: Jiakang Yuan, name: Renqiu Xia, name: Bin Wang, name: Bo Zhang, name: Lei Bai

Survey paper plays a crucial role in scientific research, especially given
the rapid growth of research publications. Recently, researchers have begun
using LLMs to automate survey generation for better efficiency. However, the
quality gap between LLM-generated surveys and those written by human remains
significant, particularly in terms of outline quality and citation accuracy. To
close these gaps, we introduce SurveyForge, which first generates the outline
by analyzing the logical structure of human-written outlines and referring to
the retrieved domain-related articles. Subsequently, leveraging high-quality
papers retrieved from memory by our scholar navigation agent, SurveyForge can
automatically generate and refine the content of the generated article.
Moreover, to achieve a comprehensive evaluation, we construct SurveyBench,
which includes 100 human-written survey papers for win-rate comparison and
assesses AI-generated survey papers across three dimensions: reference,
outline, and content quality. Experiments demonstrate that SurveyForge can
outperform previous works such as AutoSurvey.