@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://aclanthology.org/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}."
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<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 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 https://github.com/AIDC-AI/Marco-Search-Agent.</abstract>
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%0 Conference Proceedings
%T Table-as-Search: Agentic Information Seeking is Table Completion
%A Lan, Tian
%A Henry, Felix
%A Zhu, Bin
%A Jia, Qianghuai
%A Ren, Junyang
%A PU, Qihang
%A Li, Haijun
%A Wang, Longyue
%A Xu, Zhao
%A Luo, Weihua
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lan-etal-2026-table
%X 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 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 https://github.com/AIDC-AI/Marco-Search-Agent.
%U https://aclanthology.org/2026.findings-acl.743/
%P 15088-15107
Markdown (Informal)
[Table-as-Search: Agentic Information Seeking is Table Completion](https://aclanthology.org/2026.findings-acl.743/) (Lan et al., Findings 2026)
ACL
- Tian Lan, Felix Henry, Bin Zhu, Qianghuai Jia, Junyang Ren, Qihang PU, Haijun Li, Longyue Wang, Zhao Xu, and Weihua Luo. 2026. Table-as-Search: Agentic Information Seeking is Table Completion. In Findings of the Association for Computational Linguistics: ACL 2026, pages 15088–15107, San Diego, California, United States. Association for Computational Linguistics.