DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
This work addresses the need for systematic evaluation tools for LLM-based research agents, which is crucial for developers and researchers in AI and information retrieval, though it is incremental as it builds on existing agent concepts with new benchmarking methods.
The authors tackled the lack of a comprehensive benchmark for evaluating Deep Research Agents (LLM-based agents that autonomously conduct web research and generate reports) by introducing DeepResearch Bench, a benchmark with 100 PhD-level tasks across 22 fields, and proposed two novel evaluation methodologies that align well with human judgment.
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.