Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing
arXiv:2604.15771v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as a foundational paradigm for grounding large language models in external knowledge. While adaptive retrieval mechanisms have improved retrieval efficiency, existing approaches treat post-retrieval failure as a signal to retry rather than to diagnose -- leaving the structural causes of query-evidence misalignment unaddressed. We observe that a significant portion of persistent retrieval failures stem not from the absence of relevant evidence but from an alignment gap between the query and the
The continuous improvement and deployment of RAG systems highlight the need for more robust, efficient, and 'failure-aware' retrieval mechanisms as LLM applications proliferate.
Advanced RAG techniques that diagnose and address failure states directly improve the reliability, factual grounding, and overall performance of large language models, making them more commercially viable and effective for complex tasks.
Current RAG systems often iterate on retrieval attempts; this research proposes a more sophisticated approach by diagnosing the root causes of retrieval failure (skill-routing), leading to more efficient and accurate information retrieval and generation.
- · AI application developers
- · Enterprises deploying RAG-based systems
- · Open-source AI community
- · Knowledge management platforms
- · Inefficient RAG implementations
- · LLM applications prone to hallucination without robust RAG
More reliable and less 'confabulatory' AI agents due to improved information retrieval.
Reduced operational costs for AI-powered services due to fewer retrieval failures and more efficient knowledge grounding.
Acceleration in the development and adoption of AI systems for critical functions where factual accuracy is paramount.
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Read at arXiv cs.CL