CoCoEvolve: Consistency-Driven Co-Evolution for Self-Supervised Cross-Representation Learning

The paper introduces CoCoEvolve, a self-supervised framework for improving consistency among chart images, tabular data, and visualization code. It replaces ambiguous one-to-many cross-representation mappings with explicit one-to-one correspondences and uses agreement among representations as a general optimization signal without extra annotations. CoCoEvolve@Train co-evolves the chart-table-code cycle during training, while CoCoEvolve@Test applies consistency-based co-optimization at inference time. CoCoEvolve@Eval covers all six cross-representation tasks. The abstract reports improvements across four benchmarks, but provides no numerical results, dataset names, baselines, or ablation details.