用表格代替文本追踪搜索状态，让长程信息检索更稳定高效。
Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion
- 将搜索任务转化为表格补全，用行列结构管理查询与结果
- 在三种基准上显著超越现有模型，长程任务表现尤其突出
- 适合需要持续推理的智能搜索系统开发者参考
当前信息检索代理在长程探索中难以保持注意力与连贯性，因在纯文本上下文中维护搜索状态（包括规划过程和大量结果）本质上脆弱。为此，我们提出表格式搜索（Table-as-Search, TaS），一种将信息检索任务重构为表格补全的结构化规划框架。TaS将每个查询映射为外部数据库中维护的结构化表格模式，行代表候选项，列表示约束或所需信息。该表格精准管理搜索状态：填满的单元格严格记录历史与结果，空白单元格则明确表达下一步计划。关键的是，TaS统一了三类不同信息检索任务：深度搜索、广度搜索及更具挑战性的深广混合搜索。大量实验表明，TaS在三种基准上显著优于多个前沿基线，涵盖多智能体框架与商业系统。进一步分析验证了其在长程信息检索中的卓越鲁棒性，同时具备高效、可扩展、灵活等优势。代码与数据集已公开于 https://github.com/AIDC-AI/Marco-Search-Agent。
原文摘要 · Abstract (English)
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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