RAG
RAG is a research_field in our research taxonomy.
Related papers
- Structure and Diversity Aware Context Bubble Construction for Enterprise Retrieval Augmented Systems
- MiRAGE: A Multiagent Framework for Generating Multimodal Multihop Question-Answer Dataset for RAG Evaluation
- TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought
- Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access
- Retrieval, Refinement, and Ranking for Text-to-Video Generation via Prompt Optimization and Test-Time Scaling
- When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering
- JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG
- Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem
- Evaluating Social Bias in RAG Systems: When External Context Helps and Reasoning Hurts
- RAGNav: A Retrieval-Augmented Topological Reasoning Framework for Multi-Goal Visual-Language Navigation
- MultiVer: Zero-Shot Multi-Agent Vulnerability Detection
- EHR-RAG: Bridging Long-Horizon Structured Electronic Health Records and Large Language Models via Enhanced Retrieval-Augmented Generation
- T-Retriever: Tree-based Hierarchical Retrieval Augmented Generation for Textual Graphs
- Continuum Memory Architectures for Long-Horizon LLM Agents
- Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities
- MOOSEnger -- a Domain-Specific AI Agent for the MOOSE Ecosystem
- Reliability by design: quantifying and eliminating fabrication risk in LLMs. From generative to consultative AI: a comparative analysis in the legal domain and lessons for high-stakes knowledge bases
- Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration
- ES-MemEval: Benchmarking Conversational Agents on Personalized Long-Term Emotional Support
- C-GRASP: Clinically-Grounded Reasoning for Affective Signal Processing