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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.04071 |
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| _version_ | 1866911420967288832 |
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| author | Mumcu, Furkan Bekit, Lokman Jones, Michael J. Cherian, Anoop Yilmaz, Yasin |
| author_facet | Mumcu, Furkan Bekit, Lokman Jones, Michael J. Cherian, Anoop Yilmaz, Yasin |
| contents | 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 As new work continues to appear after publication, surveys quickly become outdated, contributing to redundancy and fragmentation in the literature.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04071 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic AI-Empowered Dynamic Survey Framework Mumcu, Furkan Bekit, Lokman Jones, Michael J. Cherian, Anoop Yilmaz, Yasin Machine Learning 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 As new work continues to appear after publication, surveys quickly become outdated, contributing to redundancy and fragmentation in the literature.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. |
| title | Agentic AI-Empowered Dynamic Survey Framework |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.04071 |