InfoAgent: Advancing Autonomous Information-Seeking Agents

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 finetun-ing 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. The Internet has revolutionized the way people acquire knowledge, yet the tools that mediate access to online information have evolved unevenly (Zhang et al., 2025). Recently, researchers have enhanced Large Language Models (LLMs) with agentic capabilities via Reinforcement Learning (RL), which allows them to autonomously plan, search, and learn in an ongoing loop (OpenAI, 2025b). Deep Research Agents (DRAs) are distinguished by their ability to plan, reason, execute multi-step information-seeking actions, such as retrieving documents from the Internet via given tools, and complete complex research tasks. Recognizing their potential, major AI providers have raced to deliver commercial implementations (OpenAI, 2025a; Perplexity, 2025; xAI, 2025a; Google, 2025). This phenomenon shows that deep research is becoming a defining feature of next-generation information platforms. The implementation of DRA faces two challenges: effective strategy for data synthesis and the establishment of an efficient interactive environment. Existing open-source DRAs often perform shallow searches, mainly because they are trained on relatively simple data (Jin et al., 2025; Li et al., 2025c). Training dataset must encompass a broad range of data, which is of various uncertain types, so that the agent is forced to link disparate pieces of information and infer new knowledge when retrieving documents. Meanwhile, some agents are trained in simulated environments, which are underpowered when confronted with challenging real-world problems (Jin et al., 2025).