WebWalker: Benchmarking LLMs in Web Traversal
On the map: Retrieval-augmented generation → Agentic RAG, agents and memory
Abstract (arXiv)
Retrieval-augmented generation (RAG) demonstrates remarkable performance across tasks in open-domain question-answering. However, traditional search engines may retrieve shallow content, limiting the ability of LLMs to handle complex, multi-layered information. To address it, we introduce WebWalkerQA, a benchmark designed to assess the ability of LLMs to perform web traversal. It evaluates the capacity of LLMs to traverse a website's subpages to extract high-quality data systematically. We propose WebWalker, which is a multi-agent framework that mimics human-like web navigation through an explore-critic paradigm. Extensive experimental results show that WebWalkerQA is challenging and demonstrates the effectiveness of RAG combined with WebWalker, through the horizontal and vertical integration in real-world scenarios.
Key facts. Cited by 171 papers in the map (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 44 of its 58 references that are in the map. First posted on arXiv 13 Jan 2025.
Builds on
44 of 58 references.
References · 44
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Citing papers per year: 2025: 82; 2026: 89.
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