中金：从速度到认知，AI时代的量化新生态

Core Viewpoint - The article reviews the evolution of the quantitative investment industry over the past decade, highlighting a shift from localized advantages to systemic cognitive capabilities, driven by the implementation of AI technology [1][4][12]. Industry Trends: From Speed to Cognition - The quantitative industry is transitioning towards Quant 4.0, characterized by a cognitive architecture centered on multi-agent collaboration, moving away from traditional linear models [4][12]. - Leading firms are focusing on building AI-driven mid-frequency prediction platforms, emphasizing the importance of unique high-quality data and sophisticated algorithms for sustainable excess returns [4][9][12]. Information Processing: LLM and RAG's Infrastructure Value - Large Language Models (LLMs) are transforming the processing of alternative data, significantly reducing marginal costs and enhancing the ability to extract key information from complex documents [5][26]. - Retrieval-Augmented Generation (RAG) technology addresses LLM's limitations by ensuring traceability and accuracy in quantitative strategies, enabling the capture of deeper insights [5][29]. Factor Mining: From Data Mining to Logic Generation - LLMs assist in overcoming the limitations of manual factor mining by introducing a Multi-Agent Debate framework, which enhances the quality of factors through logical generation rather than brute-force computation [6][30][36]. Structural Upgrade: From Pipeline to Cognitive Systems - The traditional linear pipeline structure in quantitative research is evolving into a multi-agent system that allows for cognitive division of labor, enhancing collaboration and accountability [7][38][41]. - Multi-Agent systems modularize the research process, improving efficiency and traceability while maintaining rigorous standards [7][41]. LLM Beyond AI Quant: Continuous Innovation - New trends in machine learning models, such as Time Series Foundation Models (TSFM) and Reinforcement Learning (RL), are emerging, emphasizing cross-asset and cross-frequency applications [8][44][46]. - TSFM enhances generalization and transfer learning capabilities, while RL optimizes decision-making in trading execution and dynamic risk management [44][46][47]. Future Outlook: Mid-Frequency as the Main Battlefield - The mid-frequency range (minute to weekly) is expected to become the primary battleground for AI technology, balancing data abundance and latency tolerance [9][50]. - Future quantitative research systems may adopt an upstream-midstream-downstream architecture, integrating real-time knowledge bases with multi-agent debate mechanisms for factor mining and execution [51][52].