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The paper introduces SAFE, a controlled benchmark to study whether AI models acquire safety-relevant evidence before acting, revealing distinct acquisition policies across frontier models like GPT-5.5, o3, and Claude variants.
DeepArrhythmia is a multimodal framework for beat-level ECG arrhythmia classification that combines raw ECG signals and waveform images, using segment-level confidence to selectively acquire physiological evidence for improved accuracy.