标题： 金融中阿尔法的演变：利用人类洞察力和大型语言模型代理 显示英文标题

标题： The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents

摘要： 超越市场基准的 alpha 收益追求经历了一场深刻的变革，从基于直觉的投资演变为主动的、由人工智能驱动的系统。 本文介绍了一个全面的五阶段分类法，追踪了这一进程，涵盖了手动策略、统计模型、经典机器学习、深度学习以及由大型语言模型（LLMs）驱动的代理架构。 与之前专注于建模技术的调查不同，本综述采用了一种系统层面的视角，整合了表征学习、多模态数据融合以及工具增强的 LLM 代理方面的进展。 强调了从静态预测器到能够实时推理、场景模拟和跨模态决策的上下文感知金融代理的战略转变。 分析了可解释性、数据脆弱性、治理和监管合规性等关键挑战，这些对于生产部署至关重要。 所提出的分类法提供了一个统一的框架，用于评估成熟度、协调基础设施，并指导下一代 alpha 系统的负责任开发。 显示英文摘要

摘要： The pursuit of alpha returns that exceed market benchmarks has undergone a profound transformation, evolving from intuition-driven investing to autonomous, AI powered systems. This paper introduces a comprehensive five stage taxonomy that traces this progression across manual strategies, statistical models, classical machine learning, deep learning, and agentic architectures powered by large language models (LLMs). Unlike prior surveys focused narrowly on modeling techniques, this review adopts a system level lens, integrating advances in representation learning, multimodal data fusion, and tool augmented LLM agents. The strategic shift from static predictors to contextaware financial agents capable of real time reasoning, scenario simulation, and cross modal decision making is emphasized. Key challenges in interpretability, data fragility, governance, and regulatory compliance areas critical to production deployment are examined. The proposed taxonomy offers a unified framework for evaluating maturity, aligning infrastructure, and guiding the responsible development of next generation alpha systems.