{"total":21,"rows":[{"id":"2412.13746","title":"RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment","created":"2024-12-18","cited_by_count":23,"area":"rag"},{"id":"2605.14473","title":"Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict","created":"2026-05-14","cited_by_count":15,"area":"rag"},{"id":"2501.02460","title":"Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications","created":"2025-01-05","cited_by_count":13,"area":"rag"},{"id":"2603.26815","title":"Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off","created":"2026-03-26","cited_by_count":13,"area":"rag"},{"id":"2504.16787","title":"Credible Plan-Driven RAG Method for Multi-Hop Question Answering","created":"2025-04-23","cited_by_count":12,"area":"rag"},{"id":"2512.19134","title":"QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation","created":"2025-12-22","cited_by_count":12,"area":"rag"},{"id":"2503.05587","title":"Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data","created":"2025-03-07","cited_by_count":10,"area":"rag"},{"id":"2512.08289","title":"MIRAGE: Misleading Retrieval-Augmented Generation via Black-box and Query-agnostic Poisoning Attacks","created":"2025-12-09","cited_by_count":4,"area":"rag"},{"id":"2503.16071","title":"Tuning LLMs by RAG Principles: Towards LLM-native Memory","created":"2025-03-20","cited_by_count":2,"area":"rag"},{"id":"2505.10792","title":"Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation","created":"2025-05-16","cited_by_count":2,"area":"rag"},{"id":"2606.25191","title":"To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAG","created":"2026-06-23","cited_by_count":2,"area":"rag"},{"id":"2509.26011","title":"RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation","created":"2025-09-30","cited_by_count":1,"area":"rag"},{"id":"2601.13227","title":"Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?","created":"2026-01-19","cited_by_count":1,"area":"rag"},{"id":"2603.25092","title":"AuthorityBench: Benchmarking LLM Authority Perception for Reliable Retrieval-Augmented Generation","created":"2026-03-26","cited_by_count":1,"area":"rag"},{"id":"2606.11257","title":"Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite","created":"2026-06-09","cited_by_count":1,"area":"rag"},{"id":"2512.00331","title":"CogEvo-Edu: Cognitive Evolution Educational Multi-Agent Collaborative System","created":"2025-11-29","cited_by_count":0,"area":"rag"},{"id":"2604.14215","title":"PriHA: A RAG-Enhanced LLM Framework for Primary Healthcare Assistant in Hong Kong","created":"2026-04-10","cited_by_count":0,"area":"rag"},{"id":"2604.17948","title":"RAVEN: Retrieval-Augmented Vulnerability Exploration Network for Memory Corruption Analysis in User Code and Binary Programs","created":"2026-04-20","cited_by_count":0,"area":"rag"},{"id":"2605.27789","title":"A Fixed-Budget, Cluster-Aware Standard for LLM-as-a-Judge Evaluation: A Multi-Hop RAG Stress Test","created":"2026-05-27","cited_by_count":0,"area":"rag"},{"id":"2606.31033","title":"CORTEX: Token-Level Hallucination Detection in RAG via Comparative Internal Representations","created":"2026-06-30","cited_by_count":0,"area":"rag"},{"id":"2609.30009","title":"Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation","created":"2026-09-24","cited_by_count":0,"area":"rag"}],"next_cursor":null}