Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests
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.
69794dcedf44b75fa47e46ef
Jingjie Ning
claimed_verified
2026-01-28T11:16:44.338Z
6310a812a23f0327bce68778
https://cdn-avatars.huggingface.co/v1/production/uploads/6310a812a23f0327bce68778/dp9JherZV-RYiSRKnvxsh.jpeg
Ethan Ning
ethanning
user
ethanning
69794dcedf44b75fa47e46f0
João Coelho
69794dcedf44b75fa47e46f1
Yibo Kong
69794dcedf44b75fa47e46f2
Yunfan Long
69794dcedf44b75fa47e46f3
Bruno Martins
69794dcedf44b75fa47e46f4
João Magalhães
69794dcedf44b75fa47e46f5
Jamie Callan
69794dcedf44b75fa47e46f6
Chenyan Xiong
Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests
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.
Large-scale analysis of LLM-powered search agent behavior reveals patterns in multi-step information seeking, evidence reuse, and session dynamics from 14.44 million search requests.
agentic search search agents multi-step information seeking log analysis DeepResearchGym sessionization query reformulation Context-driven Term Adoption Rate CTAR evidence reuse early stopping retrieval budgets cross-step context tracking