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The paper proposes Recursive Self-Improvement (RSI), a training loop in which a unified multimodal model's text and visual abilities generate training data for each other, with program execution providing verified supervision. Evaluated on charts with a new BasicChartBench benchmark, four rounds of RSI improve performance from 45.7% to 60.2% while verified-program share rises from 48.9% to 95.2%.