让论文综述像活文档一样持续更新，自动吸收新成果
Agentic AI-Empowered Dynamic Survey Framework
- 将综述视为长期维护任务，用智能代理动态更新内容
- 实验验证可有效识别并融入新研究，保持结构连贯性
- 适合需要追踪领域进展的研究者与综述撰写者
综述论文在整合科学知识方面发挥核心作用，但随着研究成果的快速增长，其滞后性日益凸显。新工作不断涌现，导致综述发布后迅速过时，造成文献冗余与碎片化。本文将综述写作重新定义为长期维护问题，而非一次性生成任务，将综述视为随研究演进而持续进化的活文档。提出一种基于智能体的动态综述框架（Agentic Dynamic Survey Framework），支持对已有综述进行增量式更新，在保留原有结构的同时最小化干扰。通过回溯实验，验证该框架能有效识别并整合新兴研究，维持综述的连贯性与结构完整性。
原文摘要 · Abstract (English)
Survey papers play a central role in synthesizing and organizing scientific knowledge, yet they are increasingly strained by the rapid growth of research output. As new work continues to appear after publication, surveys quickly become outdated, contributing to redundancy and fragmentation in the literature. We reframe survey writing as a long-horizon maintenance problem rather than a one-time generation task, treating surveys as living documents that evolve alongside the research they describe. We propose an agentic Dynamic Survey Framework that supports the continuous updating of existing survey papers by incrementally integrating new work while preserving survey structure and minimizing unnecessary disruption. Using a retrospective experimental setup, we demonstrate that the proposed framework effectively identifies and incorporates emerging research while preserving the coherence and structure of existing surveys.
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