AI summaryⓘ
The authors studied how multi-step AI systems that pull in information can sometimes make early mistakes that snowball into bigger errors later, which current methods often miss. They defined this problem as 'cascading hallucination' and created a system called CHARM to spot and stop these growing errors at multiple points during reasoning. CHARM works alongside existing AI setups without needing big redesigns and was tested on several question-answering datasets, showing it detects most cascades and greatly cuts down error spread. Their tests also showed each part of CHARM helps improve its overall accuracy. This system can help make multi-step AI tools more reliable, especially when combined with human review processes.
agentic retrieval-augmented generationcascading hallucinationmulti-step reasoningfact verificationconsistency trackingconfidence monitoringerror propagationquestion answering datasetshuman-in-the-loopAI reliability
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.