FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation
On the map: LLM reasoning → Reasoning for agents, planning and code
Abstract (arXiv)
We introduce FIRE, a comprehensive benchmark designed to evaluate both the theoretical financial knowledge of LLMs and their ability to handle practical business scenarios. For theoretical assessment, we curate a diverse set of examination questions drawn from widely recognized financial qualification exams, enabling evaluation of LLMs deep understanding and application of financial knowledge. In addition, to assess the practical value of LLMs in real-world financial tasks, we propose a systematic evaluation matrix that categorizes complex financial domains and ensures coverage of essential subdomains and business activities. Based on this evaluation matrix, we collect 3,000 financial scenario questions, consisting of closed-form decision questions with reference answers and open-ended questions evaluated by predefined rubrics. We conduct comprehensive evaluations of state-of-the-art LLMs on the FIRE benchmark, including XuanYuan 4.0, our latest financial-domain model, as a strong in-domain baseline. These results enable a systematic analysis of the capability boundaries of current LLMs in financial applications. We publicly release the benchmark questions and evaluation code to facilitate future research.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 19 of its 35 references that are in the map. First posted on arXiv 25 Feb 2026.
Builds on
19 of 35 references.
Closest ancestors · 4
Same direction, newest first
- 2025FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning16 Sep
- 2025Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning22 Jul
- 2025DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models22 Apr
- 2025ReTool: Reinforcement Learning for Strategic Tool Use in LLMs15 Apr
Other references · 15
- 2025Qwen3 Technical Reportcited by 8,195
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2024GPT-4o System Cardcited by 4,467
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- 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,678
- 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,258
- 2023BloombergGPT: A Large Language Model for Financecited by 923
- 2025DeepSeek-V3.2: Pushing the Frontier of Open Large Language Modelscited by 755
- 2025Kimi K2: Open Agentic Intelligencecited by 523
- 2025HealthBench: Evaluating Large Language Models Towards Improved Human Healthcited by 385
- 2023PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Financecited by 217
- 2025LongCat-Flash Technical Reportcited by 54
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Citing papers per year: 2026: 5.
5 papers.
- 2026From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscapecited by 4
- 2026FinResearchBench II: A Deep Research Benchmark with Consensus-Derived Gold Rubrics for Distinguishing Financial Report Qualitycited by 4
- 2026Are the Financial Reasoning from LLMs Credible? A Real World Test over Long-Horizon Statementscited by 0
- 2026Seeing Is Not Deciding: Can Multimodal LLMs Act as Effective CEOs?cited by 0
- 2026FinExam-10K: When Retrieval Helps Financial Reasoning?cited by 0
- 2026FinExam-10K: When Retrieval Helps Financial Reasoning?28 Aug 2026
- 2026Seeing Is Not Deciding: Can Multimodal LLMs Act as Effective CEOs?6 Aug 2026
- 2026Are the Financial Reasoning from LLMs Credible? A Real World Test over Long-Horizon Statements22 Jul 2026
- 2026FinResearchBench II: A Deep Research Benchmark with Consensus-Derived Gold Rubrics for Distinguishing Financial Report Quality14 Jul 2026
- 2026From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape7 Jun 2026
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