Deep research systems powered by LLM agents have transformed complex information seeking by automating the iterative retrieval, filtering, and synthesis of insights from massive-scale web sources. However, existing systems predominantly follow an autonomous "query-to-report" paradigm, limiting users to a passive role and failing to integrate their personal insights, contextual knowledge, and evolving research intents. This paper addresses the lack of human-in-the-loop collaboration in the agentic research process. Through a formative study, we identify that current systems hinder effective human-agent collaboration in terms of process observability, real-time steerability, and context navigation efficiency. Informed by these findings, we propose InterDeepResearch, an interactive deep research system backed by a dedicated research context management framework. The framework organizes research context into a hierarchical architecture with three levels (information, actions, and sessions), enabling dynamic context reduction to prevent LLM context exhaustion and cross-action backtracing for evidence provenance. Built upon this framework, the system interface integrates three coordinated views for visual sensemaking, and dedicated interaction mechanisms for interactive research context navigation. Evaluation on the Xbench-DeepSearch-v1 and Seal-0 benchmarks shows that InterDeepResearch achieves competitive performance compared to state-of-the-art deep research systems, while a formal user study demonstrates its effectiveness in supporting human-agent collaborative information seeking. Project page with system demo: https://github.com/bopan3/InterDeepResearch.

翻译：基于LLM智能体的深度研究系统通过自动化地从海量网络资源中进行迭代式检索、筛选与信息整合，彻底改变了复杂信息检索的模式。然而，现有系统主要遵循自主的“查询到报告”范式，将用户置于被动角色，未能整合其个人见解、情境知识及动态演化的研究意图。本文旨在解决智能体研究过程中人机协作环节的缺失。通过一项形成性研究，我们发现当前系统在过程可观测性、实时可引导性以及情境导航效率方面阻碍了有效的人机协作。基于这些发现，我们提出了InterDeepResearch——一个由专用研究情境管理框架支持的交互式深度研究系统。该框架将研究情境组织为包含三个层级（信息、操作与会话）的层次化架构，支持动态情境缩减以避免LLM上下文耗尽，并实现跨操作回溯以追溯证据来源。在此框架基础上，系统界面集成了三个协调视图以支持可视化感知构建，并配备了专用的交互机制以实现交互式研究情境导航。在Xbench-DeepSearch-v1和Seal-0基准测试上的评估表明，InterDeepResearch相比现有最先进的深度研究系统具有竞争优势，而正式用户研究则验证了其在支持人机协作信息检索方面的有效性。系统演示项目页：https://github.com/bopan3/InterDeepResearch。