Dr.Mi-Bench: A Modular-integrated Benchmark for Scientific Deep Research Agent

• URL: http://arxiv.org/abs/2512.00986v1
• Date: Sun, 30 Nov 2025 17:16:47 GMT
• Title: Dr.Mi-Bench: A Modular-integrated Benchmark for Scientific Deep Research Agent
• Authors: Zhihan Guo, Feiyang Xu, Yifan Li, Muzhi Li, Shuai Zou, Jiele Wu, Han Shi, Haoli Bai, Ho-fung Leung, Irwin King,
• Abstract summary: Dr.Mi-Bench is a Modular-integrated benchmark for scientific deep research (DR) agents.<n>Dr.Mi-Eval is a novel modular-integrated evaluation paradigm.
• Score: 52.876617746453995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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