CoCoEvolve: Consistency-Driven Co-Evolution for Self-Supervised Cross-Representation Learning
Original title: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.
Why it's worth reading
As analytical systems increasingly connect charts, data, and generation code, this work is timely because it applies the same consistency objective during both training and inference; the full paper is needed to verify the reported gains and costs.