arXiv:2607.04726 · v6

What You See Is What You Get: Observation-Aligned Supervision for Chart-to-Code Generation

首次提交 2026-07-06 14:53

版本更新 2026-10-28 12:52

雷达观测 2026-10-30 10:22

摘要

Chart-to-code generation is commonly trained through supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target. However, many chart programs contain latent variables that cannot be uniquely recovered from the rendered image. We identify this latent-observation mismatch in four forms across five chart types: aggregation-induced mismatch, where raw samples are reduced to box statistics or histogram bin masses; normalization-induced mismatch, where absolute scale is removed in pie charts; projection-induced mismatch, where 3D information is lost through 2D rendering; and level-set-induced mismatch, where a scalar field is observable only through selected contour lines. These mismatches introduce target ambiguity and require models to generate information unsupported by the image. We propose Observation-Aligned Supervision, which replaces latent variables with visually constrained quantities. And we study fixed-view 3D scatter and contour charts through controlled experiments. Across VLMs, observation-aligned supervision generally improves observable recovery end-to-end and under both-executable evaluation. Against compact analytic contour formulas, end-to-end performance declines despite better conditional boundary recovery in both-exec setting, suggesting a trade-off with representational compactness.