让图表生成模型只依赖可观察数据，提升代码准确性。

Chart-to-code generation is commonly trained with supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target. We argue that this assumption is often invalid: many chart programs contain latent raw variables that cannot be uniquely recovered from the rendered image. We identify this systematic latent--observation mismatch with three forms: aggregation-induced mismatch, where raw samples are reduced to summary statistics or bin-level mass; normalization-induced mismatch, where absolute scale is removed; and projection-induced mismatch, where higher-dimensional information is lost through visual projection. These mismatches introduce target ambiguity and require models to complete information unsupported by the image. We propose Observation-Aligned Supervision, which replaces latent targets with visually constrained quantities. We instantiate it for boxplots, histograms, and pie charts using box statistics, bin weights, and wedge proportions, respectively, and study projection-induced mismatch through a controlled 3D scatter experiment. Across multiple VLMs on ChartMimic and ChartX, our rewritten supervision improves observable-value recovery, including under both-executable evaluation.