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

ACL ARR 2026 January Submission3610 Authors

Keywords: Information Seeking, Agent, Structured Planning

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 will be publicly released.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: Language Modeling

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 3610