[Submitted on 16 Sep 2026]
Title:Characterizing Web Search by Conversational LLM Agents: From Search Decisions and Strategies to Results and Responses
View a PDF of the paper titled Characterizing Web Search by Conversational LLM Agents: From Search Decisions and Strategies to Results and Responses, by Mahsa Amani and 11 other authors
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Abstract:Conversational LLM agents increasingly rely on Web search, yet the end-to-end lifecycle of agentic search remains poorly understood. We present the first study of Web search across four major conversational platforms (ChatGPT, Claude, Grok, and DeepSeek), combining real-world user interactions (invivo) with controlled experiments using the same platform's models by their APIs (invitro). We investigate the quality of agentic decisions to invoke Web search, their strategies to formulate queries, the potential domain preferences in the search results they receive, and the choices they make when transforming search results into grounded responses. We find that Web-search decisions vary substantially across platforms and models, while more frequent Web-search invocation does not necessarily yield better response quality. We further show that conversational agents employ different complex querying strategies and that platform specific search engines return search results from their preferred domains. Finally, although responses are largely grounded in search results, some claims rely on uncited search results, raising concerns about attribution and reliability. Our findings have important implications for the design of future AI agents and Web search tools optimized for conversational retrieval.
Subjects:
Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
Cite as: arXiv:2609.19244 [cs.AI]
(or arXiv:2609.19244v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.19244
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mahsa Amani [view email] [v1] Wed, 16 Sep 2026 17:56:32 UTC (1,583 KB)
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