ECG-RePAIR: Agentic Test-Time Repair with Evidence Grounding for ECG Interpretation

Electrocardiography (ECG) is an essential diagnostic tool, where reliable interpretation requires clinical findings to be grounded in the underlying waveform. Recent multimodal large language models (MLLMs) have advanced automated ECG interpretation by integrating signal and image information, improving report completeness and diagnostic reasoning. However, these models still largely rely on one-pass generation, allowing uncertain or weakly grounded findings to persist in the final interpretation. In routine clinical practice, such findings are often revisited on the ECG waveform and reassessed against diagnostic criteria before an interpretation is finalized. Inspired by this practice, we propose ECG-RePAIR, an agentic test-time repair paradigm that organizes post-generation interpretation as a structured evidence-to-action process. ECG-RePAIR uses criterion-guided retrieval-augmented generation to determine what evidence should be examined, ECG tools to verify claims against the current waveform, and a repair advantage memory to determine whether the verified evidence warrants modifying the report. Extensive experiments show that ECG-RePAIR consistently improves diagnostic accuracy and report quality, while ablation studies and clinical expert evaluation further support its effectiveness.