野生环境下的代理搜索：基于1400万+真实搜索请求的意图与轨迹动态分析

摘要

基於大型語言模型的搜索代理正日益廣泛應用於多步驟信息檢索任務，然而信息檢索學界對代理式搜索會話的展開方式及檢索證據的運用機制仍缺乏實證理解。本文通過對來自DeepResearchGym（一個由外部代理客戶端訪問的開源搜索API）收集的1444萬次搜索請求（397萬個會話）進行大規模日誌分析，系統性剖析代理式搜索行為。我們採用會話化處理流程，利用基於LLM的自動標註技術分配會話級意圖標籤和逐步驟查詢重構標籤，並提出上下文驅動的術語採納率（CTAR）指標來量化新引入查詢術語與既往檢索證據的關聯程度。分析結果揭示出三類典型行為模式：首先，超過90%的多輪會話步驟數不超過十步，89%的步驟間隔在一分鐘以內；其次，不同意圖的會話呈現差異化特徵——事實查詢類會話表現出隨時間遞增的高重複性，而需推理的會話則持續保持更廣泛的探索範圍；最後，代理存在跨步驟證據復用現象，平均54%的新增查詢術語可在累積證據上下文中追溯，且早期步驟的貢獻超越最近一次檢索結果。這些發現表明，代理式搜索或可從重複感知的早期終止機制、意圖自適應的檢索資源分配以及顯式跨步驟上下文追蹤中獲益。我們計劃公開匿名化處理後的日誌數據以支持後續研究。

English

LLM-powered search agents are increasingly being used for multi-step information seeking tasks, yet the IR community lacks empirical understanding of how agentic search sessions unfold and how retrieved evidence is used. This paper presents a large-scale log analysis of agentic search based on 14.44M search requests (3.97M sessions) collected from DeepResearchGym, i.e. an open-source search API accessed by external agentic clients. We sessionize the logs, assign session-level intents and step-wise query-reformulation labels using LLM-based annotation, and propose Context-driven Term Adoption Rate (CTAR) to quantify whether newly introduced query terms are traceable to previously retrieved evidence. Our analyses reveal distinctive behavioral patterns. First, over 90% of multi-turn sessions contain at most ten steps, and 89% of inter-step intervals fall under one minute. Second, behavior varies by intent. Fact-seeking sessions exhibit high repetition that increases over time, while sessions requiring reasoning sustain broader exploration. Third, agents reuse evidence across steps. On average, 54% of newly introduced query terms appear in the accumulated evidence context, with contributions from earlier steps beyond the most recent retrieval. The findings suggest that agentic search may benefit from repetition-aware early stopping, intent-adaptive retrieval budgets, and explicit cross-step context tracking. We plan to release the anonymized logs to support future research.

野生环境下的代理搜索：基于1400万+真实搜索请求的意图与轨迹动态分析

Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests

摘要

Support