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Proposes HALO, an architecture with six layers of defense to contain hallucination in enterprise AI systems, reframing 'zero hallucination' as a system-enforced property rather than a model property.
This paper identifies a blind spot in reference-free faithfulness metrics: they only measure precision (whether claims are supported) but not recall (coverage of relevant facts). The authors introduce a complete-oracle evaluation using Formula 1 telemetry and weather data, showing that high-precision models often have poor coverage, and propose a combined metric.