The explosive growth in academic literature necessitates automated deep research (DR) agents, yet their evaluation remains a significant challenge. First, existing benchmarks often focus narrowly on retrieval while neglecting high-level planning and reasoning. Second, existing benchmarks favor general domains over the scientific domains that are the core application for DR agents. To address these gaps, we introduce Dr.Mi-Bench, a Modular-integrated benchmark for scientific DR agents. Grounded in academic literature, our benchmark uses a human-annotated dataset of 200 instances across 10 scientific domains, including both research and review papers. Besides, we also propose a Modular-integrated Evaluation Paradigm for DR Agents (Dr.Mi-Eval), a novel modular-integrated evaluation paradigm, which leverages the rich structure of academic papers to assess the core competencies of planning, retrieval, and reasoning through two complementary modes: an end-to-end evaluation for DR agents and an isolated evaluation for foundational LLMs as potential backbones. Experimental results reveal a fragmented performance landscape: agents exhibit specialized strengths but share critical weaknesses, most notably in performing the multi-source retrieval required for review-style tasks and performing consistently across diverse scientific fields. Moreover, improving high-level planning capability is the crucial factor for unlocking the reasoning potential of foundational LLMs as backbones. By exposing these actionable failure modes, Dr.Mi-Bench provides a diagnostic tool to guide the development of more reliable academic research assistants.

翻译：学术文献的爆炸式增长催生了自动化深度研究（DR）智能体，但其评估仍面临重大挑战。首先，现有基准通常仅聚焦于检索任务，而忽视了高层规划与推理能力。其次，现有基准多偏向通用领域，而非作为DR智能体核心应用场景的科学领域。为弥补这些不足，我们提出了Dr.Mi-Bench——一个面向科学领域DR智能体的模块化集成基准。该基准基于学术文献构建，采用包含10个科学领域（涵盖研究型与综述型论文）共200条实例的人工标注数据集。此外，我们同时提出了模块化集成评估范式（Dr.Mi-Eval），该创新范式利用学术论文的丰富结构，通过两种互补模式评估智能体的规划、检索与推理核心能力：面向DR智能体的端到端评估，以及面向作为潜在基座的基础大语言模型（LLMs）的隔离评估。实验结果表明当前性能呈现碎片化格局：智能体展现出专项优势，但普遍存在关键缺陷，尤其在执行综述型任务所需的多源检索时，以及在不同科学领域间保持稳定表现方面。此外，提升高层规划能力是释放基础LLMs作为基座模型推理潜力的关键因素。通过揭示这些可操作的失败模式，Dr.Mi-Bench为开发更可靠的学术研究助手提供了诊断性工具。