多模态大模型(MLLMs)

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GPT-Kline:MCoT与技术分析
HTSC· 2025-05-31 10:25
Investment Rating - The report does not explicitly state an investment rating for the industry or the specific technology discussed. Core Insights - The research explores the application of Multimodal Chain of Thought (MCoT) in investment research, particularly in technical analysis using K-line charts, leading to the development of an automated platform called GPT-Kline [1][4][13]. - MCoT enhances the reasoning capabilities of large models by combining multimodal understanding with logical reasoning, allowing for more sophisticated analysis of complex tasks [2][21]. - The O3 model, launched by OpenAI, demonstrates impressive image reasoning capabilities, marking a significant step towards achieving general artificial intelligence (AGI) [2][37]. Summary by Sections Multimodal Reasoning - Multimodal collaboration is essential for large models to progress towards AGI, requiring them to be proficient in various modalities beyond just language [17]. - MCoT represents a significant advancement, enabling models to think based on images rather than merely perceiving them [21][31]. Application in Investment Research - The report highlights the potential of MCoT in technical analysis, particularly with K-line charts, which encapsulate vital trading information and patterns suitable for analysis [3][42]. - The O3 model's application in technical analysis shows its ability to process K-line images, perform necessary pre-processing, and generate analytical reports [3][43]. Development of GPT-Kline - GPT-Kline integrates MCoT with the capabilities of large models to create a specialized tool for K-line technical analysis, automating the entire analysis process from drawing to reporting [4][65]. - The platform features a user-friendly web interface designed for intuitive interaction, allowing users to engage with the analysis process effectively [4][83]. Model Comparison and Performance - The report compares various large models, including OpenAI's GPT-4o and Gemini-2.5 series, assessing their capabilities in K-line analysis and identifying Gemini-2.5 Flash as a strong performer [66][96]. - The analysis results indicate that while OpenAI's models tend to be conservative in their outputs, the Gemini models provide more comprehensive and accurate annotations [95][96].