Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation
Quick Answer
The CHARM framework addresses cascading hallucinations in agentic RAG systems, achieving an 89.4% detection rate with only a 5.3% false positive rate, significantly reducing error propagation by 82.1%.
Quick Take
It integrates seamlessly with existing pipelines, enhancing reliability for complex reasoning tasks.
Key Points
- CHARM formalizes cascading hallucination as a distinct failure mode in systems.
- The framework includes four components for error detection and mitigation.
- Evaluated on multiple datasets, CHARM shows a significant reduction in error propagation.
- Each detection module in CHARM contributes to overall cascade coverage.
- Integrates with human oversight for enhanced reliability in AI deployments.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 04435v1 Announce Type: new Abstract: Multi-step agentic (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.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.