FinSight: Towards Real-World Financial Deep Research
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.
68f86f6b7669bcaeecce0e59
Jiajie Jin
68f86f6b7669bcaeecce0e5a
Yuyao Zhang
68f86f6b7669bcaeecce0e5b
Yimeng Xu
68f86f6b7669bcaeecce0e5c
Hongjin Qian
68f86f6b7669bcaeecce0e5d
Yutao Zhu
68f86f6b7669bcaeecce0e5e
Zhicheng Dou
FinSight: Towards Real-World Financial Deep Research
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, a multi-agent framework using CAVM architecture and iterative vision-enhanced mechanism, generates high-quality, multimodal financial reports with superior accuracy and presentation quality compared to existing systems.
multi agent framework Code Agent with Variable Memory (CAVM) programmable variable space Iterative Vision-Enhanced Mechanism Chain-of-Analysis citation-aware multimodal reports