ECG-RSI: Towards Recursive Self-Improvement via Feedback-Driven Data Synthesis

ECG multimodal language models depend on instruction data that connect waveform evidence to interpretation, while GAN- and diffusion-based generators provide complementary ways to expand signal coverage. A central challenge is to construct examples with specified ECG changes, consistent questions, and checkable evidence. We propose ECG-RSI, centered on reverse waveform perturbation and bidirectional signal–instruction synthesis. The forward route derives questions and explanations from real training-side waveforms; the reverse route translates target features or training-question distractors into constrained waveform edits, remeasures the resulting signal, and constructs supervision from the achieved evidence. Target attainment and non-target preservation jointly constrain acceptance. Two feedback loops connect this data engine to learning: construction feedback guides operator and prompt repair, while learner feedback specifies which capabilities, difficulty levels, and evidence representations to construct next. This process adjusts training data to the model's current errors and capabilities. On ECGBench, ECG-RSI raises the aggregate S7 score from 30.15 to 46.61 across four rounds, a gain of 16.46 points over the base model.