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.

翻译：当前的信息检索智能体在长视野探索过程中难以保持专注性与连贯性，其根本原因在于：将搜索状态（包括规划流程与海量搜索结果）全部置于单一纯文本上下文中进行跟踪，本质上具有脆弱性。为解决这一问题，我们提出**表格即搜索**这一结构化规划框架，将信息检索任务重新定义为表格补全任务。TaS将每个查询映射为存储于外部数据库的结构化表格模式，其中行代表搜索候选项，列表示约束条件或所需信息。该表格精确管理搜索状态：已填充单元格严格记录历史与搜索结果，而空单元格则作为显式的搜索规划。关键在于，TaS统一了三种不同的信息检索任务：深度搜索、广度搜索以及具有挑战性的深度广度联合搜索。大量实验表明，TaS在包含多智能体框架与商业系统的三类基准测试中均显著优于众多先进基线方法。此外，我们的分析验证了TaS在长视野信息检索中具有卓越的鲁棒性，同时兼具高效性、可扩展性与灵活性。代码与数据集已公开于https://github.com/AIDC-AI/Marco-Search-Agent。