FinSight：邁向現實世界的金融深度研究

摘要

生成專業的財務報告是一項耗時且對智力要求極高的過程，當前的人工智慧系統難以完全自動化這一任務。為應對這一挑戰，我們引入了FinSight（Financial InSight），這是一個新穎的多代理框架，用於生成高品質、多模態的財務報告。FinSight的核心是帶有可變記憶的程式碼代理（CAVM）架構，該架構將外部數據、設計工具和代理統一在一個可程式化的變數空間中，透過可執行程式碼實現靈活的數據收集、分析和報告生成。為確保專業級的視覺化效果，我們提出了一種迭代視覺增強機制，逐步將原始視覺輸出精煉為精緻的財務圖表。此外，一個兩階段的寫作框架將簡潔的分析鏈段擴展為連貫、引用意識強且多模態的報告，確保了分析的深度和結構的一致性。在多種公司和行業層面的任務實驗中，FinSight在事實準確性、分析深度和呈現品質方面均顯著優於所有基準系統，包括領先的深度研究系統，展示了生成接近人類專家品質報告的清晰路徑。

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：邁向現實世界的金融深度研究

FinSight: Towards Real-World Financial Deep Research

摘要

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