基于代理的动态调查框架
Agentic AI-Empowered Dynamic Survey Framework
AI总结：
本文提出基于代理的动态综述框架，通过持续整合新研究成果，保持综述的连贯性和结构，解决综述过时和碎片化问题。
摘要：
综述论文在整合和组织科学知识中起着核心作用，但随着研究产出的快速增长，它们正日益受到压力。在出版后，新工作不断出现，综述迅速过时，导致文献中的冗余和碎片化。我们将综述写作重新定义为一个长周期维护问题，而非一次性生成任务，将综述视为与所描述研究同步演化的活体文档。我们提出了一种基于代理的动态综述框架，通过逐步整合新工作来支持现有综述论文的持续更新，同时保持综述结构并最小化不必要的干扰。通过回顾性实验设置，我们证明了所提出的框架能够有效识别并整合新兴研究，同时保持现有综述的连贯性和结构。
英文摘要：
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 As new work continues to appear after publication, surveys quickly become outdated, contributing to redundancy and fragmentation in the literature.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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
