超越GPT-5、Gemini Deep Research！人大高瓴AI金融分析师，查数据、画图表、写研报样样精通

Core Viewpoint - The article introduces Yulan-FinSight, a multi-modal report generation system developed by Renmin University of China, designed to meet real financial research and investment needs, showcasing advanced capabilities in data analysis and report writing [1][3]. Group 1: Challenges of General AI in Financial Research - General AI struggles with financial reports due to their highly structured, logical, and visual nature, which involves multiple processes [5]. - Financial research demands higher data integration, analytical depth, and expression forms compared to general AI tasks [6]. - Three main challenges faced by existing general AI systems include: 1. Fragmentation of domain knowledge and data, making it difficult to integrate structured financial data with unstructured information [7]. 2. Lack of professional-level visualization capabilities, as current models can only produce basic visualizations without ensuring data consistency [8]. 3. Absence of iterative research capabilities, where existing systems follow a fixed process that limits dynamic adjustments based on intermediate findings [9]. Group 2: FinSight's Innovations - FinSight aims to emulate human financial analysts by focusing on cognitive processes and introducing three key technological innovations [10]. - The core architecture is based on a Code-Driven Variable-Memory (CAVM) multi-agent framework, allowing for collaborative reasoning through a unified variable space instead of traditional message-based communication [14][16]. - An iterative vision-enhanced mechanism is employed for generating financial charts, combining the strengths of language models for coding and visual models for feedback [20][21]. - The writing framework is restructured into a two-phase process: analysis followed by integration, ensuring clarity and depth in long reports [24][25]. Group 3: Performance and Evaluation - FinSight significantly outperformed existing deep research systems in factual accuracy, analytical depth, and presentation quality, achieving an average score of 8.09 [34]. - The system's visualization capabilities received a score of 9.00, indicating a substantial improvement in generating professional financial charts [35]. - In practical applications, FinSight produced reports averaging over 20,000 words with more than 50 charts, maintaining quality as report length increased [38]. - FinSight ranked first in the AFAC 2025 Financial Intelligence Innovation Competition, demonstrating its robustness and practical utility [39]. Group 4: Broader Implications - FinSight represents a significant advancement in AI capabilities within expert-intensive fields, suggesting that AI can now perform tasks traditionally reserved for human experts, such as problem decomposition and hypothesis validation [40][41]. - This paradigm shift indicates potential applications in various complex domains, including research analysis, legal assessment, and medical decision-making, paving the way for a new generation of productivity centered around expert-level AI agents [43].