Adding a Bayesian source memory for market-feedback adaptive retrieval to a frozen LLM improves macro-F1 from 0.438 to 0.471 and portfolio Sharpe from 0.52 to 0.84 in point-in-time financial event-impact prediction.
Yang, and Xiao-Yang Liu
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EEVEE introduces a router-based multi-dataset test-time prompt learning framework for LLM agents that uses router-prompt co-evolution to improve robustness on heterogeneous data streams.
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Point-in-Time Financial RAG with Frozen LLMs and Market-Feedback Adaptive Retrieval
Adding a Bayesian source memory for market-feedback adaptive retrieval to a frozen LLM improves macro-F1 from 0.438 to 0.471 and portfolio Sharpe from 0.52 to 0.84 in point-in-time financial event-impact prediction.