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

Skill-RAG introduces a failure-aware framework for Retrieval-Augmented Generation (RAG) that integrates a hidden-state prober and a prompt-based skill router to address misalignment between queries and evidence. By diagnosing retrieval failures and selecting from four distinct skills—query rewriting, question decomposition, evidence focusing, and an exit skill—the model enhances retrieval efficiency and accuracy, particularly on challenging open-domain QA and reasoning benchmarks. This approach is significant for practitioners as it provides a structured method to improve LLM performance in scenarios where traditional retrieval mechanisms fail, enabling more robust handling of complex queries.