Deep Research agents driven by LLMs have automated the scholarly discovery pipeline, from planning and query formulation to iterative web exploration. Yet they remain constrained by a static, ``one-size-fits-all'' retrieval paradigm. Current systems fail to adaptively adjust the depth and breadth of exploration based on the user's existing expertise or latent interests, frequently resulting in reports that are either redundant for experts or overly dense for novices. To address this, we introduce Personalized Deep Research (PDR), a framework that integrates dynamic user context into the core retrieval-reasoning loop. Rather than treating personalization as a post-hoc formatting step, PDR unifies user profile modeling with iterative query development, dual-stage (private/public) retrieval, and context-aware synthesis. This allows the system to autonomously align research sub-goals with user intent and optimize the stopping criteria for evidence collection. To facilitate benchmarking, we release the PDR Dataset, covering four realistic user tasks, and propose a hybrid evaluation framework combining lexical metrics with LLM-based judgments to assess factual accuracy and personalization alignment. Experimental results against commercial baselines demonstrate that PDR significantly improves retrieval utility and report relevance, effectively bridging the gap between generic information retrieval and personalized knowledge acquisition. The resource is available to the public at https://github.com/Applied-Machine-Learning-Lab/SIGIR2026_PDR.

翻译：由大语言模型驱动的深度研究智能体已自动化了学术发现流程，涵盖规划、查询构建及迭代式网络探索。然而，这些系统仍受限于静态"一刀切"的检索范式。现有系统无法根据用户既有专业知识或潜在兴趣自适应调整探索深度与广度，导致生成的报告对专家而言冗余重复，对新手则信息密度过高。为解决此问题，我们提出个性化深度研究（PDR）框架，该框架将动态用户情境整合至核心检索-推理循环中。PDR并非将个性化视为后期格式调整步骤，而是将用户画像建模与迭代式查询生成、双阶段（私有/公开）检索及情境感知综合统一起来。这使得系统能够自主将研究子目标与用户意图对齐，并优化证据收集的终止准则。为促进基准测试，我们发布了PDR数据集，涵盖四项真实用户任务，并提出融合词汇指标与基于大语言模型评判的混合评估框架，以评估事实准确性与个性化对齐度。与商业基线的实验结果表明，PDR显著提升了检索效用与报告相关性，有效弥合了通用信息检索与个性化知识获取之间的鸿沟。相关资源已公开于https://github.com/Applied-Machine-Learning-Lab/SIGIR2026_PDR。