金融量化团队终于有了一个不折腾大模型、只优化检索来源就能显著提升收益风险比的方案——Sharpe从0.52到0.84的跃升很实在，做事件驱动策略的可以直接参考这个贝叶斯记忆模块。
该研究提出一种面向金融事件驱动RAG的贝叶斯源记忆机制，通过市场反馈（已到期残差收益）动态更新检索来源的信任度，而不微调LLM本身。在FNSPID数据集89只纳斯达克股票上，冻结LLM+源记忆相比无记忆基线，宏F1从0.438提升至0.471，下游投资组合Sharpe比率从0.52跃升至0.84。实验表明，在金融RAG中，学会信任哪些信息来源比学会如何阅读更重要，且该方法简单模块化，可直接适配市场变化。
Learning Whom to Trust: Market-Feedback Adaptive Retrieval for Frozen LLMs in Event-Driven Financial RAG
Financial retrieval-augmented generation (RAG) systems typically rank evidence by textual relevance, but in financial markets the useful evidence source depends on event type, forecast horizon, and market context. We study news-triggered event-impact prediction as a point-in-time financial RAG problem. For each company-news anchor, the system retrieves related financial news and SEC filing passages, appends a pre-decision market-context card, and predicts multi-horizon residual-return signals. Our method keeps the large language model (LLM) reader frozen and adapts the retrieval layer through an external Bayesian source memory updated from matured residual-return feedback. On a fixed 89-stock Nasdaq-oriented universe derived from the FinRL-DeepSeek/FNSPID task, using original FNSPID news and point-in-time EDGAR filing passages, Frozen Reader with Source Memory improves held-out macro-F1 from 0.438 to 0.471 and downstream portfolio Sharpe from 0.52 to 0.84 relative to Frozen Reader with No Memory. A supervised LoRA reader improves static RAG modestly, but does not improve over the frozen source-memory reader. These results suggest that, for financial RAG, learning where to retrieve from can be as important as learning how to read, offering a simple, modular route to market-feedback adaptation.