LLMs have demonstrated significant potential in quantitative finance by processing vast unstructured data to emulate human-like analytical workflows. However, current LLM-based methods primarily follow either an Asset-Centric paradigm focused on individual stock prediction or a Market-Centric approach for portfolio allocation, often remaining agnostic to the underlying reasoning that drives market movements. In this paper, we propose a Logic-Oriented perspective, modeling the financial market as a dynamic, evolutionary ecosystem of competing investment narratives, termed Modes of Thought. To operationalize this view, we introduce MEME (Modeling the Evolutionary Modes of Financial Markets), designed to reconstruct market dynamics through the lens of evolving logics. MEME employs a multi-agent extraction module to transform noisy data into high-fidelity Investment Arguments and utilizes Gaussian Mixture Modeling to uncover latent consensus within a semantic space. To model semantic drift among different market conditions, we also implement a temporal evaluation and alignment mechanism to track the lifecycle and historical profitability of these modes. By prioritizing enduring market wisdom over transient anomalies, MEME ensures that portfolio construction is guided by robust reasoning. Extensive experiments on three heterogeneous Chinese stock pools from 2023 to 2025 demonstrate that MEME consistently outperforms seven SOTA baselines. Further ablation studies, sensitivity analysis, lifecycle case study and cost analysis validate MEME's capacity to identify and adapt to the evolving consensus of financial markets. Our implementation can be found at https://github.com/gta0804/MEME.

翻译：大型语言模型（LLM）通过处理海量非结构化数据以模拟类人的分析工作流，已在量化金融领域展现出巨大潜力。然而，当前基于LLM的方法主要遵循两种范式：一是专注于个股预测的资产中心范式，二是用于投资组合配置的市场中心范式，这两种范式通常对驱动市场变动的底层推理机制保持不可知。本文提出一种逻辑导向的视角，将金融市场建模为一个动态演化的竞争性投资叙事生态系统，称为思维模式。为实现这一视角，我们提出了MEME（金融市场演化模式建模），旨在通过演化逻辑的视角重构市场动态。MEME采用多智能体提取模块，将噪声数据转化为高保真的投资论点，并利用高斯混合模型在语义空间中揭示潜在的共识。为建模不同市场条件下的语义漂移，我们还实现了时序评估与对齐机制，以追踪这些模式的生命周期和历史盈利能力。通过优先考虑持久的市场智慧而非短暂异常，MEME确保投资组合构建受到稳健推理的指导。在2023年至2025年三个异构中国股票池上的大量实验表明，MEME持续优于七个SOTA基线方法。进一步的消融研究、敏感性分析、生命周期案例研究和成本分析验证了MEME识别并适应金融市场演化共识的能力。我们的实现可在https://github.com/gta0804/MEME找到。