FISHNET用多智能体协同分析万份财报，提升金融洞察准确率。
FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning
- 分步拆解任务，多智能体协作处理复杂财务数据
- 对超9.8万份文件分析，洞察生成成功率达61.8%
- 适合金融风控、投研等需高精度数据处理的场景
从海量数据源生成金融智能通常依赖传统知识图谱构建或数据库工程。近期，微调的金融领域大语言模型（LLMs）崭露头角。尽管前景可观，但高推理成本、幻觉问题以及对高维财务数据并发分析的复杂性仍存。为此，我们提出FISHNET（Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert swarming, and Task planning），一种代理架构，可完成对超过98,000份在语义、数据层级和格式上差异极大的监管文件的复杂分析任务。FISHNET在金融洞察生成上表现卓越（成功率达61.8%，路由成功率5.0%，RAG R-Precision为45.6%）。通过严谨的消融实验，我们实证了各代理的重要性及整体架构优化效果。其模块化设计可适配多种场景，具备可扩展性、灵活性与数据完整性，满足金融任务核心需求。
原文摘要 · Abstract (English)
Financial intelligence generation from vast data sources has typically relied on traditional methods of knowledge-graph construction or database engineering. Recently, fine-tuned financial domain-specific Large Language Models (LLMs), have emerged. While these advancements are promising, limitations such as high inference costs, hallucinations, and the complexity of concurrently analyzing high-dimensional financial data, emerge. This motivates our invention FISHNET (Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert swarming, and Task planning), an agentic architecture that accomplishes highly complex analytical tasks for more than 98,000 regulatory filings that vary immensely in terms of semantics, data hierarchy, or format. FISHNET shows remarkable performance for financial insight generation (61.8% success rate over 5.0% Routing, 45.6% RAG R-Precision). We conduct rigorous ablations to empirically prove the success of FISHNET, each agent's importance, and the optimized performance of assembling all agents. Our modular architecture can be leveraged for a myriad of use-cases, enabling scalability, flexibility, and data integrity that are critical for financial tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。