GroundedCache reduces unsafe-served rate in RAG answer caching to 0-1.5% (vs 15-51.5% naive) via four validation gates while keeping p50 latency within 1.07x of no-cache baseline.
2501.03468 , archivePrefix =
6 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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2026 6verdicts
UNVERDICTED 6representative citing papers
RAG models exhibit a monitoring-control gap: they acknowledge epistemic conflicts in accumulating documents yet fail to constrain unsafe recommendations, with single-turn tests overestimating safety.
DQA maintains persistent diagnostic state and aggregates retrievals at the root-cause level to reach 78.7% success on 150 enterprise IT scenarios versus 41.3% for standard multi-turn RAG while cutting average turns from 8.4 to 3.9.
RAG-DIVE uses an LLM to dynamically generate, validate, and evaluate multi-turn dialogues for assessing RAG system performance in interactive settings.
Small language models can run RAG generation on-device without GPUs in reasonable time.
5ting achieves nDCG@5 of 0.4719 on Task A and harmonic score 0.5597 with RL_F 0.7692 on Task C for multi-turn RAG via standard dense retrieval plus LLM reranking and faithfulness constraints.
citing papers explorer
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Grounded Cache Routing for Retrieval-Augmented Generation: When Is It Safe to Reuse an Answer?
GroundedCache reduces unsafe-served rate in RAG answer caching to 0-1.5% (vs 15-51.5% naive) via four validation gates while keeping p50 latency within 1.07x of no-cache baseline.
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Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs
RAG models exhibit a monitoring-control gap: they acknowledge epistemic conflicts in accumulating documents yet fail to constrain unsafe recommendations, with single-turn tests overestimating safety.
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DQA: Diagnostic Question Answering for IT Support
DQA maintains persistent diagnostic state and aggregates retrievals at the root-cause level to reach 78.7% success on 150 enterprise IT scenarios versus 41.3% for standard multi-turn RAG while cutting average turns from 8.4 to 3.9.
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RAG-DIVE: A Dynamic Approach for Multi-Turn Dialogue Evaluation in Retrieval-Augmented Generation
RAG-DIVE uses an LLM to dynamically generate, validate, and evaluate multi-turn dialogues for assessing RAG system performance in interactive settings.
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Little Brains, Big Feats: Exploring Compact Language Models
Small language models can run RAG generation on-device without GPUs in reasonable time.
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5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control
5ting achieves nDCG@5 of 0.4719 on Task A and harmonic score 0.5597 with RL_F 0.7692 on Task C for multi-turn RAG via standard dense retrieval plus LLM reranking and faithfulness constraints.