Papers
1–4 of 4Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration
Graph-based Retrieval-Augmented Generation (GraphRAG) frameworks face a trade-off between the comprehensiveness of global search and the efficiency of local search. Existing methods are often challeng...
Reason and Verify: A Framework for Faithful Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) significantly improves the factuality of Large Language Models (LLMs), yet standard pipelines often lack mechanisms to verify inter- mediate reasoning, leaving the...
Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval
Retrieval-Augmented Generation (RAG) systems typically treat documents as flat text, ignoring the structured metadata and linked relationships that knowledge graphs provide. In this paper, we investig...
TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) helps large language models (LLMs) answer knowledge-intensive and time-sensitive questions by conditioning generation on external evidence. However, most RAG syste...