@inproceedings{chen-etal-2026-beyond-single,
    title = "Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision",
    author = "Chen, Bingsen  and
      Li, Boyan  and
      Nie, Ping  and
      Zhang, Yuyu  and
      Ye, Xi  and
      Zhao, Chen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.609/",
    pages = "13325--13356",
    ISBN = "979-8-89176-390-6",
    abstract = "Existing benchmarks for Deep Research Agents (DRAs) treat report generation as a single-shot writing task, which fundamentally diverges from how human researchers iteratively draft and revise reports via self-reflection or peer feedback. Whether DRAs can reliably revise reports with user feedback remains unexplored. We introduce Mr Dre, an evaluation suite that establishes multi-turn report revision as a new axis. Mr Dre consists of (1) a unified long-form report evaluation protocol spanning comprehensiveness, factuality, and presentation, and (2) a human-verified feedback simulation pipeline for systematic multi-turn revision evaluation. Our analysis of five diverse DRAs reveals a critical limitation: while agents can address most user feedback, they also regress on 16{--}27{\%} of previously covered content and citation quality. Over multiple revision turns, even the best-performing agents leave significant headroom, as they continue to disrupt content outside the feedback{'}s scope and fail to preserve earlier edits. We further show that these issues are not easily resolvable through inference-time fixes such as prompt engineering and a dedicated sub-agent for revision."
}