InfoAgent: Advancing Autonomous Information-Seeking Agents
Authors: name: Gongrui Zhang, name: Jialiang Zhu, name: Ruiqi Yang, name: Kai Qiu, name: Miaosen Zhang, name: Zhirong Wu, name: Qi Dai, name: Bei Liu, name: Chong Luo, name: Zhengyuan Yang, name: Linjie Li, name: Lijuan Wang, name: Weizhu Chen, name: Yuan Zhang, name: Xin Li, name: Zhaoyi Liu, name: Xin Geng, name: Baining Guo
Organization: Microsoft

Building Large Language Model agents that expand their capabilities by
interacting with external tools represents a new frontier in AI research and
applications. In this paper, we introduce InfoAgent, a deep research agent
powered by an innovative data synthesis pipeline and orchestrated web search
tools. To construct challenging, hard-to-find queries,we build entity trees and
apply sub-tree sampling with entity fuzzification to systematically increase
question difficulty. Unlike prior work that relies heavily on commercial search
tools, we develop a dedicated self-hosted search infrastructure, enhancing
transparency of agent environments and facilitating further advancement of
agent capacity. We evaluate the effectiveness of our data pipeline by measuring
the average number of tool calls required to correctly answer a question, and
also show that our agent yields better performance when equipped with our
tools. Our InfoAgent is post-trained from Qwen3-14B using a two-stage
recipe: cold-start supervised finetuning to instill long-horizon search
behaviors, followed by reinforcement learning which significantly improves
reasoning-driven tool use. With our methods, InfoAgent achieves 15.3\% accuracy
on BrowseComp, 29.2\% on BrowseComp-ZH, and 40.4\% on Xbench-DS, outperforming
prior open-source deep research agents such as WebSailor-72B and DeepDive-32B.