这项研究戳穿了搜索智能体的真实能力——它们可能只是在验证已知信息而非真正搜索。做搜索Agent或评估AI检索能力的团队，值得看看LiveBrowseComp这个新基准，避免被静态测试误导。
研究团队发现，基于LLM的搜索智能体在BrowseComp基准测试中，高达44.5%的问题无需工具即可回答，超过一半的搜索查询来自内部假设而非检索线索，表现出对内在知识的依赖而非真正的证据驱动搜索。当移除支持答案的外部证据后，智能体表现甚至不如闭卷基线。为此，团队推出了LiveBrowseComp，一个包含335个依赖90天内发布事实的问题的深度搜索基准，所有智能体在该基准上的闭卷准确率低于2%，搜索增强得分比BrowseComp下降25-40点，且模型排名不再可靠。该基准旨在评估智能体超越内在知识覆盖的真实搜索能力。
LiveBrowseComp: Are Search Agents Searching, or Just Verifying What They Already Know?
Are LLM-based search agents genuinely searching, or using the web to verify what they already know? We study this question on BrowseComp with three diagnostics. Our analysis reveals Intrinsic Knowledge Dependence (IKD): even with tool access, agents often rely on intrinsic knowledge -- information encoded in the model before retrieval -- rather than on external evidence. Agents answer up to 44.5% of BrowseComp questions without tools, generate more than half of their search queries from internally produced hypotheses rather than retrieved leads, and perform worse than closed-book baselines when answer-supporting evidence is removed. These results suggest that static search benchmarks can reward memory-backed verification rather than evidence-driven discovery, conflating what agents already know with what they can find. We then introduce LiveBrowseComp, a deep-search benchmark designed to evaluate agents beyond intrinsic coverage. It contains 335 human-authored questions whose answers depend on facts published within the 90 days preceding benchmark construction, drawn from six updated sources and filtered to exclude globally salient events. On LiveBrowseComp, all evaluated agents fall below 2% closed-book accuracy, search-augmented scores drop by 25-40 points relative to BrowseComp, and prior model rankings no longer reliably predict performance. LiveBrowseComp is available at https://huggingface.co/datasets/Forival/LiveBrowseComp.