Skill-RAG
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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. This paper introduces Skill-RAG, a failure-state-aware retrieval system that uses hidden-state probing to detect when an LLM is approaching a knowledge failure, then routes the query to a specialized retrieval strategy matched to the gap.
Hidden-state probing as a retrieval trigger: Skill-RAG trains a lightweight probe on the LLM's hidden representations that predicts whether the model is about to fail the query. Only queries that clear the probe's failure threshold trigger retrieval, which cuts unnecessary search calls while preserving answers for the cases that actually need help.
Skill-matched retrieval strategies: Different failure modes (factual recall, multi-hop reasoning, temporal knowledge) are routed to different retrieval "skills" rather than a single generic retriever. Each skill is treated as a standalone component the agent can select between, echoing the broader trend of turning RAG into a collection of composable primitives.
Consistent gains across benchmarks: Evaluated on HotpotQA, Natural Questions, and TriviaQA, Skill-RAG improves over uniform RAG baselines on both efficiency and accuracy. The efficiency story matters as much as the accuracy: per-query retrieval cost drops significantly when the system skips retrieval for questions the model can already answer.
A shift in how RAG is designed: The work reinforces the direction RAG is heading: from a single monolithic pipeline to a suite of retrieval skills an agent selects between. Knowing when to retrieve and what kind of retrieval to run is becoming the central design question.
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