Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion

• URL: http://arxiv.org/abs/2602.06724v1
• Date: Fri, 06 Feb 2026 14:18:26 GMT
• Title: Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion
• Abstract summary: We introduce textbfTable-as-Search (TaS), a structured planning framework that reformulates the InfoSeeking task as a Table Completion task.<n>TaS unifies three distinct InfoSeeking tasks: Deep Search, Wide Search, and the challenging DeepWide Search.
• Score: 37.18779486132463
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/AIDC-AI/Marco-Search-Agent.

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