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.
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MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.
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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.
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MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.