The study examines how conversational LLM agents—specifically ChatGPT, Claude, Grok, and DeepSeek—use Web search, combining real user interactions with controlled API experiments. It finds that agents differ in when they decide to search, how they craft queries, and which domains they favor, and that more frequent searching does not always improve answer quality. While most responses are grounded in search results, some claims are unsupported, raising attribution concerns.
arXiv:2510. 11560v2 Announce Type: replace-cross Abstract: The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, coherent response.
arXiv:2606. 12924v1 Announce Type: new Abstract: We present a modular two-agent simulation framework for evaluating conversational shopping assistant architectures.
arXiv:2607.10198v2 Announce Type: replace Abstract: Search APIs expose ranked snippets, URLs, and metadata on which agents decide whether to answer, search again, or fetch pages. We evaluate these in...
arXiv:2605.16428v4 Announce Type: replace-cross Abstract: Search engines traditionally complement online content platforms by directing users seeking information to external websites. The emergence o...
arXiv:2605. 12887v2 Announce Type: replace-cross Abstract: Web-enabled LLM agents are changing how online information influences search outcomes.
arXiv:2607. 07652v1 Announce Type: cross Abstract: Search engines have long allocated attention on the web by routing users from queries to websites.
Q2D-Web is a new large‑scale benchmark for agentic Retrieval‑Augmented Generation (RAG) systems, featuring a 190 million‑document web corpus and 70 k machine‑reformulated search queries in ten languages. It supplies three sets of relevance judgments—agent citations, production rankings, and a combined set enriched with LLM‑based labels—to evaluate first‑stage retrievers. Experiments on 13 retrievers show consistent ranking across judgment sets but significant variation across domains, languages, and query types, and demonstrate that a carefully sampled sub‑corpus can approximate full‑corpus evaluation with minimal loss in Recall@1000.
arXiv:2606. 12871v1 Announce Type: new Abstract: Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses.
arXiv:2603.27476v3 Announce Type: replace Abstract: AI-powered people search platforms are increasingly deployed for recruiting, sales prospecting, and professional networking, yet no standardized be...
The paper introduces SearchWeaver, a framework that automatically optimizes search interfaces for language-model agents by revising corpus artifacts, executable tools, and usage instructions based on execution trajectories and task rewards. By tailoring the interface to each corpus, the method improves answer accuracy from 71.6% to 78.4% on BrowseComp-Plus and raises average nDCG@10 from 45.7 to 55.0 across four BRIGHT subsets. These results demonstrate the benefit of jointly optimizing interface components to enhance agentic search performance.
arXiv:2608.23045v1 Announce Type: new Abstract: Web search agents powered by Large Language Models (LLMs) show strong promise, but deep research tasks expose a recurring failure mode: once an agent h...
arXiv:2512.23044v3 Announce Type: replace Abstract: Autonomous agents are shifting information seeking from passive retrieval toward proactive, open-ended research, yet this progress has largely bypa...