# FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysis arXiv: https://arxiv.org/abs/2510.13936 ยท HTML: https://mlsanity.com/paper/2510.13936/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. - Builds on 17 of its 27 references that are in the map. - First posted on arXiv 15 Oct 2025. ## Placed in - LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/) ## Builds on (most cited first, 17 of 17) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025) - [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023) - [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025) - [GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models](https://mlsanity.com/paper/2508.06471/) (2025) - [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025) - [Evaluating the Performance of Large Language Models on GAOKAO Benchmark](https://mlsanity.com/paper/2305.12474/) (2023) - [AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?](https://mlsanity.com/paper/2407.15711/) (2024) - [MMSearch-R1: Incentivizing LMMs to Search](https://mlsanity.com/paper/2506.20670/) (2025) - [ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition](https://mlsanity.com/paper/2503.21248/) (2025) - [Deep Research: A Survey of Autonomous Research Agents](https://mlsanity.com/paper/2508.12752/) (2025) - [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://mlsanity.com/paper/2506.18959/) (2025) - [FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning](https://mlsanity.com/paper/2509.13160/) (2025) - [ExpertLongBench: Benchmarking Language Models on Expert-Level Long-Form Generation Tasks with Structured Checklists](https://mlsanity.com/paper/2506.01241/) (2025) - [DeepResearch Arena: The First Exam of LLMs' Research Abilities via Seminar-Grounded Tasks](https://mlsanity.com/paper/2509.01396/) (2025) - [FinResearchBench: A Logic Tree based Agent-as-a-Judge Evaluation Framework for Financial Research Agents](https://mlsanity.com/paper/2507.16248/) (2025) - [ScholarSearch: Benchmarking Scholar Searching Ability of LLMs](https://mlsanity.com/paper/2506.13784/) (2025) ## Built on by (most cited first, 14 of 14) - [Agent-as-a-Judge](https://mlsanity.com/paper/2601.05111/) (2026) - [DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity](https://mlsanity.com/paper/2602.11685/) (2026) - [FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains](https://mlsanity.com/paper/2601.12259/) (2026) - [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026) - [FinDeepForecast: A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecasting](https://mlsanity.com/paper/2601.05039/) (2026) - [FinMTM: A Multi-Turn Multimodal Benchmark for Financial Reasoning and Agent Evaluation](https://mlsanity.com/paper/2602.03130/) (2026) - [FinTrace: Holistic Trajectory-Level Evaluation of LLM Tool Calling for Long-Horizon Financial Tasks](https://mlsanity.com/paper/2604.10015/) (2026) - [JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks](https://mlsanity.com/paper/2602.06486/) (2026) - [FinDeepIndicator: Benchmarking Deep Research Agents in End-to-End Financial Indicator Construction](https://mlsanity.com/paper/2608.00764/) (2026) - [WANDR: A Benchmark for Wide and Deep Research](https://mlsanity.com/paper/2608.14747/) (2026) - [F2Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading](https://mlsanity.com/paper/2608.05668/) (2026) - [Mint-Agent: Introducing Finance-Native Agentic Foundation Models](https://mlsanity.com/paper/2608.16386/) (2026) - [DelistBench: Evaluating Search-Enabled LLMs for Auditable Corporate-Event Database Completion](https://mlsanity.com/paper/2608.22770/) (2026) - [LifeSearch: Benchmarking Deep Research Agents for Real-World Life and Productivity](https://mlsanity.com/paper/2610.18092/) (2026) ## Abstract (arXiv) 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/.