ID2C: EVALUATING AND ADVANCING DOCUMENT-TO-CHART GENERATION WITH IMPLICIT REQUIREMENTS

Generating a chart from a document requires more than locating values and writing plotting code: a system must also infer how the values should be transformed and which visual form best communicates the requested analysis. Existing text-to-chart benchmarks often make these decisions explicit, leaving their treatment by current models poorly understood. We introduce ID2C, a human-filtered benchmark of 1,000 implicit document-to-chart requests drawn from real documents and balanced across ten analytical task types. Each request leaves the construction procedure unspecified and is paired with a sample-specific rubric covering data selection, value transformation, chart-form selection, and visual encoding. Evaluation of open and proprietary models reveals persistent weaknesses in value transformation and chart-form selection, even when the generated charts are executable and visually polished. To address these failures, we construct a benchmark-independent visualization catalogue containing 199 chart forms and 744 value transformations, with each entry describing when and how it should be applied. We integrate this external knowledge source into a retrieval-augmented multi-agent harness whose form agent retrieves relevant catalogue entries before deciding the transformation and chart form. Experiments across multiple models show that the proposed harness consistently improves chart-generation quality, with the largest gains on the two planning decisions targeted by the catalogue.