Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion
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.
6989aa63beecc443208d27ce
Tian Lan
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2026-02-09T14:31:30.189Z
627ca439a09edfe62239c671
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Tian Lan
GMFTBY
user
GMFTBY
6989aa63beecc443208d27cf
Felix Henry
6989aa63beecc443208d27d0
Bin Zhu
6989aa63beecc443208d27d1
Qianghuai Jia
6989aa63beecc443208d27d2
Junyang Ren
6989aa63beecc443208d27d3
Qihang Pu
6989aa63beecc443208d27d4
Haijun Li
6989aa63beecc443208d27d5
Longyue Wang
6989aa63beecc443208d27d6
Zhao Xu
6989aa63beecc443208d27d7
Weihua Luo
Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion
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.
Table-as-Search framework reformulates information seeking tasks as table completion problems, improving long-horizon search robustness through structured state management.
information seeking table completion search states structured planning deep search wide search deepwide search multi-agent framework search agent