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].

MCoT团队 投稿 量子位 | 公众号 QbitAI 多模态思维链 （MCoT） 系统综述来了！ 不仅阐释了与该领域相关的基本概念和定义，还包括详细的分类法、对不同应用中现有方法的分析、对当前挑战的洞察以及促进多模态推理发 展的未来研究方向。 当下，传统思维链 （CoT） 已经让AI在文字推理上变得更聪明，比如一步步推导数学题的答案。但现实世界远比单一文字复杂得多——我们 看图说话、听声辨情、摸物识形。 MCoT的出现就像给AI装上了"多感官大脑"，它 能同时处理图像、视频、音频、3D模型、表格等多种信息 。比如，输入一张CT影像和患者的 病史，AI就能输出诊断报告，还能标注出病灶位置。 这种跨越模态的推理能力，让AI更接近人类的思考方式。 然而，尽管取得了这些进展，该领域仍缺乏全面综述。为了填补这一空白，来自新加坡国立大学、香港中文大学、新加坡南洋理工大学、罗切 斯特大学的研究人员联合完成这项新工作。 以下是更多细节。 MCoT核心方法论 多模态思维链 （MCoT） 的成功依赖于其系统化的方法论体系，以下是对其六大技术支柱的重新表述与润色，旨在提升学术表达的精确性与 流畅性： 1、推理构建视角 基于提示 （ ...