FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation
#FIRE benchmark #Large Language Models #Financial intelligence #AI evaluation #XuanYuan 4.0 #Financial reasoning #Artificial intelligence research #Qualification exams
📌 Key Takeaways
- FIRE is a comprehensive benchmark for evaluating LLMs' financial intelligence and reasoning capabilities
- The benchmark includes both theoretical assessment using financial exam questions and practical evaluation through business scenarios
- Researchers evaluated state-of-the-art LLMs including their own XuanYuan 4.0 financial-domain model
- The benchmark and evaluation code have been publicly released to facilitate future research
📖 Full Retelling
A team of researchers led by Xiyuan Zhang and including 10 co-authors introduced FIRE on February 25, 2026, a comprehensive benchmark designed to evaluate both the theoretical financial knowledge of Large Language Models (LLMs) and their ability to handle practical business scenarios in the rapidly evolving field of artificial intelligence. The FIRE benchmark addresses the critical need for standardized evaluation tools in the intersection of AI and finance, providing researchers and developers with a robust framework to assess financial AI capabilities. The benchmark consists of two main components: theoretical assessment and practical evaluation, creating a holistic approach to measuring financial intelligence in AI systems.
For the theoretical component, researchers curated a diverse set of examination questions drawn from widely recognized financial qualification exams, enabling a thorough evaluation of LLMs' deep understanding and application of financial concepts. The practical component features a systematic evaluation matrix that categorizes complex financial domains and ensures comprehensive coverage of essential subdomains and business activities. Based on this matrix, the team collected 3,000 financial scenario questions, including closed-form decision questions with reference answers and open-ended questions evaluated by predefined rubrics. This dual approach provides a more complete picture of how AI systems perform in financial contexts.
The researchers conducted comprehensive evaluations of state-of-the-art LLMs using the FIRE benchmark, with XuanYuan 4.0, their latest financial-domain model, serving as a strong in-domain baseline. These evaluations enabled a systematic analysis of the capability boundaries of current LLMs in financial applications. In a move to foster further research, the team publicly released the benchmark questions and evaluation code, making this valuable resource accessible to the broader AI research community. This development marks a significant step forward in establishing standardized evaluation methods for AI systems in specialized domains like finance, potentially accelerating advancements in financial AI applications while ensuring their reliability and effectiveness.
🏷️ Themes
Artificial Intelligence, Financial Technology, Benchmark Development, Evaluation Methodology
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Financial intelligence
Intelligence assessment of accounting and financial transactions
Financial intelligence (FININT) is the gathering of information about the financial affairs of entities of interest, to understand their nature and capabilities, and predict their intentions. Generally the term applies in the context of law enforcement and related activities. One of the main purpose...
Entity Intersection Graph
Connections for Large language model:
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Educational technology
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Reinforcement learning
3 shared
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Machine learning
2 shared
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Artificial intelligence
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Benchmark
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22273 [Submitted on 25 Feb 2026] Title: FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation Authors: Xiyuan Zhang , Huihang Wu , Jiayu Guo , Zhenlin Zhang , Yiwei Zhang , Liangyu Huo , Xiaoxiao Ma , Jiansong Wan , Xuewei Jiao , Yi Jing , Jian Xie View a PDF of the paper titled FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation, by Xiyuan Zhang and 10 other authors View PDF HTML Abstract: 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. Subjects: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG) Cite as: arXiv:2602.22273 [cs.AI] (or arXiv:2602.22273v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22273 Focus to learn more arXiv-issued DOI via DataCite Submission histo...
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