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The paper evaluates Jev, a non-generative decision model, against a frontier LLM (gpt-6-astra) for RAG claim verification on 495 LLM-AggreFact claims, finding statistically indistinguishable headline accuracy at a 187-fold cost difference but highly unequal error profiles, and showing that a confidence-gated cascade can cut costs without beating a matched random-escalation control.
This paper presents a confidence-gated hybrid system for emotion recognition in conversational AI that routes most traffic through a low-cost ensemble and escalates uncertain cases to an LLM, achieving high accuracy while reducing costs and latency for CCaaS platforms.
Discusses how cascading recovery and feedback loops can cause metastable failures in distributed systems, preventing proper recovery.