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Training such specialized models requires large volumes of high-quality task data, which motivates the need for synthetic data generation for agentic search. BrowseComp has become a widely-used benchmark for evaluating such capabilities, consisting of challenging yet easily verifiable deep research tasks. However, its reliance on dynamic web content makes evaluation non-reproducible across time. BrowseComp-Plus addresses this by pairing each task with a static corpus of positive documents and distractors, enabling reproducible evaluation, though the manual curation process limits scalability. WebExplorer’s “explore and evolve” pipeline offers a more scalable alternative: an explorer agent collects facts on a seed topic until it can construct a challenging question, then an evolution step obfuscates the query to increase difficulty. While fully automated, this pipeline lacks a verification mechanism to ensure the accuracy of generated document pairings. This is critical for training data, in which label noise directly degrades model quality. Additionally, existing synthetic generation methods have mostly been applied in the web search domain, leaving open whether they can scale across the diverse range of domains where agentic search is deployed.