Doctor-RAG: A Failure-Aware Repair Framework for Agentic Retrieval-Augmented Generation

Abstract

Agentic Retrieval-Augmented Generation interleaves retrieval and reasoning for multi-hop QA and complex knowledge tasks. As reasoning trajectories lengthen, failures become more frequent, while existing methods often either stop at diagnosis or rely on coarse replanning and rerun-style recovery, incurring high computational cost. We propose DoctorRAG (DR-RAG), a diagnose-and-repair framework that corrects failures via explicit error localization and prefix reuse. DR-RAG operates in two stages: (i) trajectory-level failure diagnosis, where a distilled diagnosis model jointly assesses evidence sufficiency, classifies the failure type, and localizes the earliest failure point; and (ii) tool-conditioned local repair that intervenes only at the diagnosed point while reusing conditionally valid prefixes and retrieved evidence. By separating error attribution from correction, DR-RAG avoids blind reruns in a post-hoc repair setting and enables targeted, efficient correction of known failed trajectories. Experiments on three multi-hop QA benchmarks across multiple agentic RAG baselines and backbone models show substantial improvements in answer accuracy.