Deep Research (DR) agents, powered by advanced Large Language Models (LLMs), have recently garnered increasing attention for their capability in conducting complex research tasks. However, existing literature lacks a rigorous and systematic evaluation of DR Agent's capabilities in critical research analysis. To address this gap, we first propose HisRubric, a novel evaluation framework with a hierarchical analytical structure and a fine-grained grading rubric for rigorously assessing DR agents' capabilities in corporate financial analysis. This framework mirrors the professional analyst's workflow, progressing from data recognition to metric calculation, and finally to strategic summarization and interpretation. Built on this framework, we construct a FinDeepResearch benchmark that comprises 64 listed companies from 8 financial markets across 4 languages, encompassing a total of 15,808 grading items. We further conduct extensive experiments on the FinDeepResearch using 16 representative methods, including 6 DR agents, 5 LLMs equipped with both deep reasoning and search capabilities, and 5 LLMs with deep reasoning capabilities only. The results reveal the strengths and limitations of these approaches across diverse capabilities, financial markets, and languages, offering valuable insights for future research and development. The benchmark and evaluation code is publicly available at https://OpenFinArena.com/.
@article{arxiv.2510.13936,
title = {FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysis},
author = {Fengbin Zhu and Xiang Yao Ng and Ziyang Liu and Chang Liu and Xianwei Zeng and Chao Wang and Tianhui Tan and Xuan Yao and Pengyang Shao and Min Xu and Zixuan Wang and Jing Wang and Xin Lin and Junfeng Li and Jingxian Zhu and Yang Zhang and Wenjie Wang and Fuli Feng and Richang Hong and Huanbo Luan and Ke-Wei Huang and Tat-Seng Chua},
journal= {arXiv preprint arXiv:2510.13936},
year = {2026}
}