@inproceedings{lan-etal-2026-table,
    title = "Table-as-Search: Agentic Information Seeking is Table Completion",
    author = "Lan, Tian  and
      Henry, Felix  and
      Zhu, Bin  and
      Jia, Qianghuai  and
      Ren, Junyang  and
      PU, Qihang  and
      Li, Haijun  and
      Wang, Longyue  and
      Xu, Zhao  and
      Luo, Weihua",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.743/",
    pages = "15088--15107",
    ISBN = "979-8-89176-395-1",
    abstract = "Current Information Seeking (InfoSeeking) agents struggle to maintain focus and coherence during long-horizon exploration, as tracking search states, including planning procedure and massive search results, within one plain-text context is inherently fragile.To address this, we introduce \textbf{Table-as-Search (TaS)}, a structured planning framework that reformulates the InfoSeeking task as a Table Completion task.TaS maps each query into a structured table schema maintained in an external database, where rows represent search candidates and columns denote constraints or required information.This table precisely manages the search states: filled cells strictly record the history and search results, while empty cells serve as an explicit search plan.Crucially, TaS unifies three distinct InfoSeeking tasks: Deep Search, Wide Search, and the challenging DeepWide Search.Extensive experiments demonstrate that TaS significantly outperforms numerous state-of-the-art baselines across three kinds of benchmarks, including multi-agent framework and commercial systems.Furthermore, our analysis validates the TaS{'}s superior robustness in long-horizon InfoSeeking, alongside its efficiency, scalability and flexibility.Code and datasets are publicly released at \url{https://github.com/AIDC-AI/Marco-Search-Agent}."
}