FinSight: Towards Real-World Financial Deep Research

Professional financial reports are the cornerstone of investment decision-making, demanding deep analytical reasoning and multimodal synthesis.While recent deep research systems excel in open-domain search tasks, they struggle with financial reporting, specifically in processing structured financial data, ensuring analytical depth, and integrating professional visualizations.To address this gap, we introduce FinSight (Financial InSight), the first multi-agent framework for end-to-end automation of professional multimodal financial reports.At its core, we propose a Code Agent with Variable Memory architecture, unifying financial data, domain tools, and agent modules into a programmable variable space to enable flexible data manipulation and reasoning via executable code.To guarantee report quality, FinSight adopts a Two-Stage Writing Framework with Generative Retrieval.It first distills raw data into structured Chain-of-Analysis segments, then synthesizes them into a coherent, citation-aware, and multimodal narrative complying with financial reporting norms.Additionally, an Iterative Vision-Enhanced Mechanism uses visual feedback to refine codegenerated charts to expert standards.Experiments on company and industry-level tasks demonstrate that FinSight significantly outperforms state-of-the-art deep research systems in factual accuracy, analytical depth, and presentation quality, validating the effectiveness of our framework.