A provenance-guided multi-agent pipeline with synthetic evaluation suppresses false positives in remote patient monitoring.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Generative AI evaluation must shift from static benchmark scores to measuring sustained improvements in human capabilities within specific deployment contexts.
citing papers explorer
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Veritas-RPM: Provenance-Guided Multi-Agent False Positive Suppression for Remote Patient Monitoring
A provenance-guided multi-agent pipeline with synthetic evaluation suppresses false positives in remote patient monitoring.
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Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility
Generative AI evaluation must shift from static benchmark scores to measuring sustained improvements in human capabilities within specific deployment contexts.