SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing
Jing Chen, Zhiheng Yang, Yixian Shen, Jie Liu, Adam Belloum, Chrysa Papagainni, Paola Grosso

TL;DR
SurveyGen-I is an automated framework that enhances scientific survey generation by integrating retrieval, adaptive planning, and memory-guided writing to improve coherence and citation coverage across multi-section surveys.
Contribution
It introduces a novel memory-guided, adaptive planning approach for LLM-based survey generation, addressing coherence and coverage limitations of prior methods.
Findings
Outperforms previous methods in content quality
Achieves higher coherence across survey sections
Provides more comprehensive citation coverage
Abstract
Survey papers play a critical role in scientific communication by consolidating progress across a field. Recent advances in Large Language Models (LLMs) offer a promising solution by automating key steps in the survey-generation pipeline, such as retrieval, structuring, and summarization. However, existing LLM-based approaches often struggle with maintaining coherence across long, multi-section surveys and providing comprehensive citation coverage. To address these limitations, we introduce SurveyGen-I, an automatic survey generation framework that combines coarse-to-fine retrieval, adaptive planning, and memory-guided generation. SurveyGen-I first performs survey-level retrieval to construct the initial outline and writing plan, and then dynamically refines both during generation through a memory mechanism that stores previously written content and terminology, ensuring coherence…
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Taxonomy
TopicsSurvey Methodology and Nonresponse
