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AI Economist Agent：结合 RAG、知识图谱与大语言模型的证据驱动经济金融分析智能体框架
arXiv 预印本（v2，2026-09-10）提出 AI 经济学家智能体框架，用于经济与金融情景分析。该框架由 LLM 智能体负责规划分析、检索证据并组织经济机制，由注册的量化模型生成数值结果，并通过预定义测试判断中间结果能否用于最终报告。作者将其应用于欧洲宏观金融压力情景与银行资本分析，在历史信息截断条件下评估经济机制检索、情景构建、模型执行与报告生成。摘要未披露具体量化结果或基线对比。
AI推荐理由 arXiv 预印本，作者 Masahiro Kato，v2 修订于 2026-09-10。提出结合 RAG、知识图谱与 LLM 智能体的经济金融情景分析框架：LLM 智能体负责规划分析、检索证据与组织经济机制，注册的量化模型生成数值结果，预定义测试判定中间结果能否进入最终报告；应用于欧洲宏观金融压力情景与银行资本分析，并在历史信息截断条件下评估机制检索、情景构建、模型执行与报告生成。局限在于仅为预印本，摘要未给出量化指标或与基线对比，'基于证据'与'可追溯来源'为作者自述，尚待同行评审与复现验证。
72
正文
Economics > General Economics
arXiv:2606.20041 (econ)
[Submitted on 18 Jun 2026 (v1), last revised 10 Sep 2026 (this version, v2)]
Title:AI Economist Agent: An Agentic Framework for Evidence-Based Economic and Financial Analysis with RAG, Knowledge Graphs, and Large Language Models
Authors:Masahiro Kato
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Abstract:We propose an AI economist agent for economic and financial scenario analysis. Scenario design often requires analysts to assess emerging risks with limited historical precedent, combine information from many sources, and translate qualitative mechanisms into internally consistent quantitative paths. Large language models (LLMs) can search and synthesize this information, but fluent narratives alone do not establish the model-based calculations needed for economic conclusions. Our framework uses LLM agents to plan the analysis, retrieve relevant evidence, and organize economic mechanisms, while registered quantitative models generate numerical outcomes and predefined tests determine whether intermediate results can be used in the final report. We apply the framework to European macro-financial stress scenarios and bank capital analysis. The empirical analysis evaluates retrieval of economic mechanisms, scenario construction, model execution, and report generation under a historical information cutoff. The results show how the AI economist agent can combine flexible evidence retrieval and scenario construction while keeping the resulting analysis linked to identifiable sources and explicit model calculations.
Subjects:
General Economics (econ.GN); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); General Finance (q-fin.GN)
Cite as:
arXiv:2606.20041 [econ.GN]
(or
arXiv:2606.20041v2 [econ.GN] for this version)
https://doi.org/10.48550/arXiv.2606.20041
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arXiv-issued DOI via DataCite
Submission history
From: Masahiro Kato [view email]
[v1]
Thu, 18 Jun 2026 10:18:03 UTC (126 KB)
[v2]
Thu, 10 Sep 2026 09:25:51 UTC (119 KB)
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