![[2602.17450] Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.17450] Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research
Summary
This paper explores the evolution of web research through generative-retrieval architectures, highlighting the transformative impact of large language models (LLMs) on information retrieval and related applications.
Why It Matters
As LLMs reshape the landscape of web research, understanding their integration into generative-retrieval architectures is crucial for researchers and practitioners. This study provides insights into current advancements, challenges, and future directions, making it relevant for those in AI and information retrieval fields.
Key Takeaways
- LLMs are revolutionizing web research by transforming traditional information retrieval methods.
- Generative-retrieval architectures enable new applications like web summarization and educational tools.
- The study identifies key developments and challenges in integrating LLMs into web solutions.
- Future directions for enhancing web research with LLMs are discussed, providing a roadmap for practitioners.
- Understanding these advancements is essential for staying competitive in the evolving AI landscape.
Computer Science > Information Retrieval arXiv:2602.17450 (cs) [Submitted on 19 Feb 2026] Title:Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research Authors:Amirereza Abbasi, Mohsen Hooshmand View a PDF of the paper titled Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research, by Amirereza Abbasi and 1 other authors View PDF Abstract:Web research and practices have evolved significantly over time, offering users diverse and accessible solutions across a wide range of tasks. While advanced concepts such as Web 4.0 have emerged from mature technologies, the introduction of large language models (LLMs) has profoundly influenced both the field and its applications. This wave of LLMs has permeated science and technology so deeply that no area remains untouched. Consequently, LLMs are reshaping web research and development, transforming traditional pipelines into generative solutions for tasks like information retrieval, question answering, recommendation systems, and web analytics. They have also enabled new applications such as web-based summarization and educational tools. This survey explores recent advances in the impact of LLMs-particularly through the use of retrieval-augmented generation (RAG)-on web research and industry. It discusses key developments, open challenges, and future directions for enhancing web solutions with LLMs. Subjects: Information Retrieval (cs.IR); A...
