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This paper formalizes the 'Granularity Paradox' in time-series forecasting, showing that finer temporal disaggregation improves in-sample fit but degrades out-of-sample accuracy due to recursive error propagation, and benchmarks multiple models across granularities.
This paper studies how reasoning exchange among multiple AI agents can improve accuracy but also risk error propagation, proposing a runtime monitoring framework to prevent such propagation.
Introduces energy conservation as a hard physical constraint on inter-module information flow in modular neural networks, enforcing exact preservation of activation energy at module boundaries to attenuate error propagation. Experiments on CIFAR-10 and a robotic pipeline show significant improvements in noise robustness.
This paper introduces CHARM, a framework for detecting and mitigating cascading hallucinations in multi-step agentic RAG pipelines, where early-stage errors propagate and amplify across reasoning steps. CHARM achieves an 89.4% cascade detection rate and 82.1% error propagation reduction across multiple benchmarks with low latency overhead.
A developer discusses cascading failures in a 3-agent SDR system, where hallucinations propagate through agents, and seeks advice on improving reliability with human-in-loop or framework switching.