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

Key points

Existing retrieval-augmented generation systems often diagnose post-retrieval failure poorly and respond with generic retries instead of addressing the root cause.
Persistent retrieval failures often arise from a structural mismatch between the user query and the available evidence, such as broad queries or multi-hop questions, which cannot be fixed by repeated retrieval alone.
Traditional adaptive RAG approaches lack fine-grained, failure-conditioned control and cannot distinguish different types of retrieval mismatch or the model's internal failure state.
Skill-RAG uses a recurrent pipeline with a hidden-state prober that detects whether the LLM is in a failure state based on its internal representation after retrieval.
When failure is detected, a prompt-based skill router diagnoses the specific cause of the query-evidence mismatch and selects one of four predefined target retrieval skills, such as query rewriting, question decomposition, or evidence focusing.
The selected skill performs corrective actions such as restructuring the query and triggering a new retrieval round, and the loop continues until the prober indicates sufficiency, a termination skill is chosen, or the maximum number of rounds is reached.

External research summaries. These are not HDATF publications or measured product results.