表格即搜索：将长程智能信息探索任务构建为表格补全问题

摘要

当前的信息检索智能体在长周期探索中难以保持专注与连贯性，因为仅依靠纯文本上下文来追踪搜索状态（包括规划流程和海量搜索结果）存在固有脆弱性。为解决这一问题，我们提出表格式搜索框架，将信息检索任务重新定义为表格填充任务。该框架将每个查询映射为存储于外部数据库的结构化表格范式，其中行代表搜索候选项，列表示约束条件或所需信息。这种表格能精准管理搜索状态：已填充单元格严格记录历史与搜索结果，而未填充单元格则作为明确的搜索计划。关键突破在于，该框架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混合搜索。大量实验表明，在包含多智能体框架和商业系统的三类基准测试中，该框架显著优于众多前沿基线方法。进一步分析验证了该框架在长周期信息检索中卓越的鲁棒性，同时兼具高效性、可扩展性和灵活性。代码与数据集已开源：https://github.com/AIDC-AI/Marco-Search-Agent。

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 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.

表格即搜索：将长程智能信息探索任务构建为表格补全问题

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

摘要

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