@inproceedings{xu-etal-2026-sage,
    title = "{SAGE}: Steerable Agentic Data Generation for Deep Search with Execution Feedback",
    author = "Xu, Fangyuan  and
      Han, Rujun  and
      Chen, Yanfei  and
      Wang, Zifeng  and
      Hsu, I-Hung  and
      Yan, Jun  and
      Tirumalashetty, Vishy  and
      Choi, Eunsol  and
      Pfister, Tomas  and
      Lee, Chen-Yu",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.174/",
    pages = "3334--3351",
    ISBN = "979-8-89176-386-9",
    abstract = "Deep search agents, which aim to answer complex questions requiring reasoning across multiple documents, can significantly speed up the information-seeking process. Collecting human annotations for this application is prohibitively expensive due to long and complex exploration trajectories. We propose an agentic pipeline that automatically generates high-quality, difficulty-controlled deep search question-answer pairs for a given corpus and a target difficulty level. Our pipeline, SAGE, consists of a data generator which proposes QA pairs and a search agent which attempts to solve the generated question and provide execution feedback for the data generator. The two components interact over multiple rounds to iteratively refine the question-answer pairs until they satisfy the target difficulty level. Our intrinsic evaluation shows SAGE generates questions that require diverse reasoning strategies, while significantly increases the correctness and difficulty of the generated data. Our extrinsic evaluation demonstrates up to 23{\%} relative performance gain on popular deep search benchmarks by training deep search agents with our synthetic data. Additional experiments show that agents trained on our data can adapt from fixed-corpus retrieval to Google Search at inference time, without further training."
}