Agentic AI-Empowered Dynamic Survey Framework

TL;DR

This paper introduces an agentic framework for continuously updating survey papers, transforming them into living documents that evolve with ongoing research to reduce redundancy and maintain relevance.

Contribution

It presents a novel agentic dynamic survey framework that enables incremental updates to survey papers, addressing the challenge of keeping surveys current amidst rapid research growth.

Findings

Framework effectively identifies and incorporates new research.

Maintains survey coherence and structure during updates.

Demonstrates successful retrospective experimental validation.

Abstract

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 As new work continues to appear after publication, surveys quickly become outdated, contributing to redundancy and fragmentation in the literature.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.