STAR: Semantic-Tuned and Tail-Adaptive Retriever for Graph-Augmented Generation

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

arXiv:2609.03874v1 Announce Type: new Abstract: Retrieval Augmented Generation (RAG) is a key component for generating accurate and hallucination free answers using Large Language

arXiv:2607.26071v1 Announce Type: cross Abstract: In this work, we propose GuidedRAG, a novel extension to traditional Retrieval-Augmented Generation (RAG) that introduces a

arXiv:2605.12975v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing

arXiv:2609.18099v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (RAG) can help answer questions that require information from many documents. However,

arXiv:2604.08553v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic

arXiv:2603.22942v1 Announce Type: new Abstract: Translating Natural Language to SQL (NL2SQL) remains a critical bottleneck for democratization of data in enterprises. Although