@inproceedings{choubey-etal-2026-dont,
    title = "Don{'}t Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination",
    author = "Choubey, Prafulla Kumar  and
      Huang, Kung-Hsiang  and
      Venkit, Pranav Narayanan  and
      Zhang, Jiaxin  and
      Vats, Vaibhav  and
      Li, Yu  and
      Peng, Xiangyu  and
      Wu, Chien-Sheng",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.116/",
    pages = "1699--1713",
    ISBN = "979-8-89176-394-4",
    abstract = "Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a scalable Enterprise Deep Research (EDR) architecture to address these failures. Our system (i) decomposes requests into coverage-driven objectives via outline generation with reflection, (ii) localizes context with dependency-guided execution and explicit information sharing, and (iii) enforces evidence-based completion criteria so agents iteratively collect information until sufficiency conditions are met. We evaluate on an internal sales enablement task and the public DeepResearch Bench benchmark, where our proposed system design achieves the strongest overall performance compared with competitive deep-research baselines. The results show that dependency-controlled context and explicit evidence sufficiency criteria reduce premature stopping and improve the consistency and depth of enterprise research outputs."
}