面向智能体搜索的交互空间检索

摘要

面向搜索智能体的检索技术仍沿袭自非智能体信息检索：检索器对语料库进行排序，智能体则读取少量返回文档。近期直接语料库交互（DCI）研究表明，智能体可通过grep和文件读取等shell工具直接操作原始语料库。但无界交互难以扩展：任何宽泛的shell命令都需扫描整个语料库，且随着语料库增长，延迟性能急剧恶化。我们认为，面向智能体搜索的检索作用不仅是筛选适合大语言模型上下文窗口的文档，更是构建一个交互空间：语料库中一个带关联工具可供智能体探索的有界子集。这一观点带来两个设计启示：该空间需要由检索提供边界，且空间内的对象需经交互化处理。作为概念验证，我们提出RISE（检索交互空间）：采用BM25构建交互空间，同时在索引阶段对文档进行shell式导航处理。在BrowseComp-Plus数据集上，RISE配合gpt-5.4-mini达到78%的准确率，与纯shell的DCI基线持平，而每次查询成本降低约四分之三。当语料库规模达100万文档时，RISE-BM25配合gpt-5.4-mini达到81%准确率，而DCI配合gpt-5.4-nano则因100次实验中出现33次挂钟超时失效，准确率下降至60%。

English

Retrieval for search agents is still inherited from non-agentic information retrieval: a retriever ranks the corpus and the agent reads a small set of returned documents. Recent direct corpus interaction (DCI) work shows that agents can instead interact with the raw corpus through shell tools such as grep and file reads. But unbounded interaction does not scale: every broad shell command is a scan over the whole corpus, and latency degrades sharply as the corpus grows. We argue that the role of retrieval for agentic search is not just to select documents that fit in the LLM context window, but to construct an interaction space: a bounded subset of the corpus the agent can explore with associated tools. Two design consequences follow. The space needs a boundary supplied by retrieval, and the objects within it should be processed for interaction. As a proof of concept, we propose RISE (Retrieving Interaction SpacE): we use BM25 to construct the interaction space; meanwhile, its documents are processed during indexing for shell-style navigation. On BrowseComp-Plus, RISE matches the pure-shell DCI baseline at 78% accuracy with gpt-5.4-mini at roughly one quarter of the per-query cost. At 1M documents, RISE-BM25 reaches 81% on gpt-5.4-mini, whereas DCI on gpt-5.4-nano degrades to 60% with 33 of 100 wall-clock failures.