What it measures
30,000 questions over 5,485 complex data-visualization infographics combining text, graphics, layout, and numerical charts.
Reading the measurement
Study what information the model receives and which inference the answer requires. A question may mix recall, visual perception, deduction and search. Use task breakdowns to find the mechanism behind a headline gain.
Published evaluation
Test-set average normalized Levenshtein similarity for M4C model variants. Visual features, detector tuning, selected object features and OCR budgets are source configuration variables.
Where this fits in an evaluation suite
Ask what the task actually requires: recall, deduction, search, or a mixture. Separate closed-book reasoning from retrieval and tool-assisted problem solving.
Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.
A reproducible comparison
Test-set average normalized Levenshtein similarity for M4C model variants. Visual features, detector tuning, selected object features and OCR budgets are source configuration variables.
Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.
PRIMARY EVIDENCE & FURTHER READING
Reviewed 2026-10-26. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.