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.

翻译：多步智能体检索增强生成（RAG）流水线在复杂推理任务中展现出显著能力，但仍易受现有幻觉检测机制系统性遗漏的一类失效模式影响：即级联幻觉——早期流水线阶段引入的错误会在后续推理步骤中传播放大，产生自信但事实性错误的最终输出。针对这一脆弱性，我们正式将级联幻觉定义为智能体RAG系统中的独特失效模式，提出四类级联模式分类体系，并引入CHARM（级联幻觉感知解析与缓解）架构框架，用于检测和中断多步推理流水线中的错误传播。CHARM包含四个组件——阶段级事实验证、跨阶段一致性追踪、置信度传播监控及级联解析触发——它们可与标准智能体RAG流水线协同运行，无需替换架构。我们在HotpotQA、MuSiQue、2WikiMultiHopQA及自定义对抗数据集上，基于LangChain智能体流水线配置评估CHARM，实现了89.4%的级联检测率（5.3%假阳性率），每阶段平均延迟开销215毫秒±18毫秒，错误传播减少82.1%，而输出级检测器仅为18.5%。组件消融实验证实，每个检测模块均对整体级联覆盖率有显著贡献。CHARM可与人在环监督框架集成，为生产级智能体AI部署提供完整的可靠性与治理栈。