🤖 AI Summary
This work addresses the challenge of cascading hallucinations in multi-step agent-based RAG systems, where early errors propagate confidently yet factually incorrect outputs, and existing approaches struggle to detect such failures effectively. The study formalizes cascading hallucination as a distinct failure mode and introduces CHARM—a lightweight, plug-and-play framework that mitigates error propagation through four mechanisms: stage-level factual verification, cross-stage consistency tracking, confidence propagation monitoring, and cascade-aware resolution triggering. Experimental results demonstrate that CHARM achieves an 89.4% detection rate for cascading errors with a low false positive rate of 5.3%, reduces error propagation by 82.1%, and incurs only a modest average latency increase of 215ms per reasoning stage across multiple multi-hop question answering benchmarks, substantially outperforming baseline methods that solely inspect final outputs.
📝 Abstract
Multi-step agentic retrieval-augmented generation (RAG) pipelines have demonstrated significant capability for complex reasoning tasks, yet remain vulnerable to a class of failure that existing hallucination detection mechanisms systematically miss: cascading hallucination, where errors introduced at early pipeline stages propagate and amplify across successive reasoning steps, producing confident but factually incorrect final outputs. To address this vulnerability, we formalize cascading hallucination as a distinct failure mode in agentic RAG systems, present a four-type taxonomy of cascade patterns, and introduce CHARM (Cascading Hallucination Aware Resolution and Mitigation), an architectural framework for detecting and interrupting error propagation in multi-step reasoning pipelines. CHARM comprises four components - stage-level fact verification, cross-stage consistency tracking, confidence propagation monitoring, and cascade resolution triggering - that operate alongside standard agentic RAG pipelines without requiring architectural replacement. We evaluate CHARM on HotpotQA, MuSiQue, 2WikiMultiHopQA, and a custom adversarial dataset across LangChain agentic pipeline configurations, achieving an 89.4% cascade detection rate with a 5.3% false positive rate and 215 ms +/- 18 ms average latency overhead per stage, achieving an error propagation reduction of 82.1%, compared to 18.5% for output-level detectors. Component ablations confirm that each detection module contributes meaningfully to overall cascade coverage. CHARM integrates with human-in-the-loop oversight frameworks to provide a complete reliability and governance stack for production agentic AI deployment.