When Failures Propagate: Causal Failure Attribution in Agentic Retrieval-Augmented Generation

The paper introduces AgenticRAG-FP, an interventional benchmark designed to attribute causal failures in agentic retrieval‑augmented generation (RAG) systems. By injecting a certified fault at a specified hop and re‑executing the downstream trajectory, the benchmark evaluates whether post‑hoc diagnostics can correctly identify the fault’s location. Experiments on MuSiQue questions show that coverage‑based diagnosis performs well at hop 1 but poorly at later hops, while counterfactual probes reveal varying diagnostic success depending on propagation depth.

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• llms
• rag
• agents
• benchmarks