An evidence-grounded LLM-as-Designer agent refines ECG classifiers from failure cases and deterministic measurements, freezing a stronger deployable model with ~10% relative macro-F1 gains.
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Failures Reveal What Metrics Miss: An Evidence-Driven Agent for Recursive Refinement of ECG Classifiers
An evidence-grounded LLM-as-Designer agent refines ECG classifiers from failure cases and deterministic measurements, freezing a stronger deployable model with ~10% relative macro-F1 gains.