Echo-α integrates organ-specific detectors with global visual context via an invoke-and-reason agentic loop, trained on a nine-task curriculum plus sequential RL, to achieve superior grounding (56.73%/43.78% F1@0.5) and diagnosis (74.90%/49.20% accuracy) on cross-center renal and breast ultrasound.
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Echo-{\alpha}: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretation
Echo-α integrates organ-specific detectors with global visual context via an invoke-and-reason agentic loop, trained on a nine-task curriculum plus sequential RL, to achieve superior grounding (56.73%/43.78% F1@0.5) and diagnosis (74.90%/49.20% accuracy) on cross-center renal and breast ultrasound.