DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
Authors: name: Mingxuan Du, name: Benfeng Xu, name: Chiwei Zhu, name: Xiaorui Wang, name: Zhendong Mao

Deep Research Agents are a prominent category of LLM-based agents. By
autonomously orchestrating multistep web exploration, targeted retrieval, and
higher-order synthesis, they transform vast amounts of online information into
analyst-grade, citation-rich reports--compressing hours of manual desk research
into minutes. However, a comprehensive benchmark for systematically evaluating
the capabilities of these agents remains absent. To bridge this gap, we present
DeepResearch Bench, a benchmark consisting of 100 PhD-level research tasks,
each meticulously crafted by domain experts across 22 distinct fields.
Evaluating DRAs is inherently complex and labor-intensive. We therefore propose
two novel methodologies that achieve strong alignment with human judgment. The
first is a reference-based method with adaptive criteria to assess the quality
of generated research reports. The other framework is introduced to evaluate
DRA's information retrieval and collection capabilities by assessing its
effective citation count and overall citation accuracy. We have open-sourced
DeepResearch Bench and key components of these frameworks at
https://github.com/Ayanami0730/deep_research_bench to accelerate the
development of practical LLM-based agents.