这篇论文提出CoCoEvolve，不用人工标注就能让模型对齐图表、表格和代码，四个基准上都有提升，做多模态可视化数据的人可以看看。
CoCoEvolve是一种新的自监督学习方法，用于提升图表图像、表格数据和可视化代码之间的跨表示一致性。该方法将跨表示映射定义为显式的一对一对应关系，通过表示间的一致性优化模型，无需额外标注。训练时CoCoEvolve@Train在图表-表格-代码循环中进行协同进化，测试时CoCoEvolve@Test在推理阶段应用相同的一致性目标。CoCoEvolve@Eval评估套件覆盖全部六种跨表示任务，在四个基准上同时提升了训练期和测试期性能。
Consistency-Driven Co-Evolution for Self-Supervised Cross-Representation Learning
As chart images, tabular data, and visualization code play increasingly important roles across diverse domains, cross-representation understanding across these modalities poses fundamental challenges for AI systems: the relationships across representations are inherently \textit{one-to-many}, supervision is ambiguous and costly, and model optimization lacks a principled signal that is both direction-adaptive and representation-generalizable beyond task-specific objectives. We introduce CoCoEvolve to improve consistency across chart, table, and code representations. Instead of treating cross-representation mapping as a one-to-many problem, we define explicit one-to-one correspondences and optimize models using agreement between representations, without additional annotations. During training, CoCoEvolve@Train performs co-evolution across the chart-table-code cycle, while CoCoEvolve@Test applies the same consistency objective at inference time for test-time co-optimization. We also present CoCoEvolve@Eval, an evaluation suite covering all six cross-representation tasks. Across four benchmarks, CoCoEvolve improves performance in both training-time and test-time settings. Our project page: https://xhguo7.github.io/CoCoEvolve/.