Tag
CIFQA introduces a deterministic tool-grounded multi-agent LLM framework for financial query answering that separates linguistic interpretation from numerical execution, achieving high accuracy and outperforming larger models on calculation-intensive tasks.
This paper introduces HC-RAG, a hierarchical cross-modal retrieval-augmented generation framework for evidence-centric financial question answering over 10-K filings, along with a new benchmark Multi-Doc-2025. It outperforms RAPTOR and GraphRAG on financial QA benchmarks, especially for long-document and table-related queries.
This paper introduces FinAgent-RAG, an agentic framework for financial document question answering that combines iterative retrieval, Program-of-Thought reasoning, and adaptive resource allocation to improve accuracy and reduce costs.