@omarsar0: Nice paper combining the strength of Skills and RAG. Most RAG systems retrieve on every query, whether the model needs …

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rag skill-rag retrieval-augmented-generation llm-optimization research-paper

Summary

Research introduces Skill-RAG, a novel approach that combines Skills with Retrieval-Augmented Generation to address inefficiencies in traditional RAG systems that retrieve on every query regardless of whether the model actually needs the information.

Nice paper combining the strength of Skills and RAG. Most RAG systems retrieve on every query, whether the model needs help or not. This is wasteful when the model already knows the answer, and often too late when it does not. New research introduces Skill-RAG, a
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Cached at: 04/21/26, 07:08 AM

Nice paper combining the strength of Skills and RAG. Most RAG systems retrieve on every query, whether the model needs help or not. This is wasteful when the model already knows the answer, and often too late when it does not. New research introduces Skill-RAG, a

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Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing

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Skill-RAG is a failure-aware RAG framework that uses hidden-state probing and skill routing to diagnose and correct query-evidence misalignment in retrieval-augmented generation. The approach detects retrieval failures and selectively applies targeted skills (query rewriting, question decomposition, evidence focusing) to improve accuracy on hard cases and out-of-distribution datasets.

ScalableRAG: High-Quality RAG at Zero Ingestion Cost

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This paper introduces ScalableRAG, a retrieval-augmented generation method that achieves high accuracy without any ingestion costs (no vector database or knowledge graph) by using regex-based set creation and aggregative reasoning. It outperforms baselines on multiple datasets and also presents a limited-ingestion variant for further accuracy improvements.