Researchers have introduced CoCoEvolve, a novel framework designed to facilitate self-supervised cross-representation learning across chart images, tabular data, and visualization code. Addressing the inherent one-to-many mapping challenges and the high cost of manual annotations, the method avoids traditional task-specific objectives in favor of optimizing explicit one-to-one correspondences between modalities. The approach leverages representation agreement as a principled signal to ensure consistency across the three formats. The framework operates in two distinct modes: CoCoEvolve@Train, which performs co-evolution through a cyclic chart-table-code training process, and CoCoEvolve@Test, which applies the consistency objective during inference for test-time co-optimization. To validate the method, the authors developed CoCoEvolve@Eval, a specialized evaluation suite encompassing six cross-representation tasks. Experimental results across four different benchmarks demonstrate that CoCoEvolve enhances performance in both training-time and test-time settings, proving more effective at capturing the underlying relationships between visual, structured, and programmatic data representations without requiring additional supervision.