FinSight：迈向现实世界的金融深度研究

摘要

生成专业的财务报告是一项劳动密集且对智力要求极高的过程，当前的人工智能系统难以完全实现自动化。为应对这一挑战，我们引入了FinSight（金融洞察），一种创新的多智能体框架，用于制作高质量、多模态的财务报告。FinSight的核心是带有可变记忆的代码智能体（CAVM）架构，该架构将外部数据、设计工具及智能体统一到一个可编程的变量空间中，通过可执行代码实现灵活的数据收集、分析及报告生成。为确保专业级的可视化效果，我们提出了一种迭代视觉增强机制，逐步将原始视觉输出精炼为精美的财务图表。此外，一个两阶段的写作框架将简洁的分析链片段扩展为连贯、引用意识强且多模态的报告，保证了分析的深度与结构的一致性。在各类公司及行业层面的任务实验中，FinSight显著超越了所有基线系统，包括领先的深度研究系统，在事实准确性、分析深度及呈现质量方面均表现出色，展示了生成接近人类专家水平报告的清晰路径。

English

Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, we introduce FinSight (Financial InSight), a novel multi agent framework for producing high-quality, multimodal financial reports. The foundation of FinSight is the Code Agent with Variable Memory (CAVM) architecture, which unifies external data, designed tools, and agents into a programmable variable space, enabling flexible data collection, analysis and report generation through executable code. To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism that progressively refines raw visual outputs into polished financial charts. Furthermore, a two stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports, ensuring both analytical depth and structural consistency. Experiments on various company and industry-level tasks demonstrate that FinSight significantly outperforms all baselines, including leading deep research systems in terms of factual accuracy, analytical depth, and presentation quality, demonstrating a clear path toward generating reports that approach human-expert quality.

FinSight：迈向现实世界的金融深度研究

FinSight: Towards Real-World Financial Deep Research

摘要

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