Agentic Retrieval-Augmented Generation for Financial Document Question Answering

arXiv:2605.05538v1 Announce Type: new Abstract: We present AgenticRAG, a practical agentic harness for retrieval and analysis over enterprise knowledge bases. Standard RAG

arXiv:2605.05482v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly being adopted across various domains. However, their adoption in banking industry faces

arXiv:2603.22305v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment

arXiv:2605.00257v1 Announce Type: cross Abstract: The inception of Large Language Models (LLMs) has catalyzed AI adoption in the finance sector, yet their reliability in complex,

arXiv:2608.26114v1 Announce Type: new Abstract: Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical

arXiv:2606.11537v1 Announce Type: new Abstract: Financial and tabular question answering requires more than fluent reasoning: answers must be grounded in the exact facts,

arXiv:2605.18770v1 Announce Type: cross Abstract: We present a collaborative agentic GraphRAG framework for expert analysis of commercial registry data. Public registries are

arXiv:2606.17642v1 Announce Type: new Abstract: Financial multimodal reasoning requires agents to coordinate numerical computation, retrieval, visual interpretation, and temporal