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README.md

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+# E2 Benchmark
+
+An efficiency benchmark of 180 VQA cases in two sweeps. Each sweep varies one workload dimension
+while holding the others fixed, so latency can be measured against that axis. Token counts use
+LLaVA-1.5's tokenizer and prompt format. Images are unmodified, so visual tokens are constant only for
+fixed-resolution models.
+
+| `category`                | varied axis (`level`)        | levels        | cases | held fixed                         |
+|---------------------------|------------------------------|---------------|-------|------------------------------------|
+| `input_text_tokens`       | prompt tokens                | 16 (57 to 74) | 80    | answer 1 to 11 tokens              |
+| `reference_answer_tokens` | gold answer tokens           | 20 (1 to 21)  | 100   | prompt 59 to 63 tokens             |
+
+Each level has 5 distinct cases.
+
+## Sources
+
+| dataset | split            | cases |
+|---------|------------------|-------|
+| AI2D    | test             | 8     |
+| ChartQA | test (human)     | 37    |
+| DocVQA  | val              | 65    |
+| GQA     | testdev balanced | 23    |
+| TextVQA | val              | 47    |
+
+AI2D questions that refer to diagram letters use the ABC-labelled version of the diagram (`images/AI2D_abc/`).
+AI2D answers are given as option text, not letters.
+
+## Layout
+
+```
+varest_benchmark.json    # all cases
+images/<dataset>/    # source images
+```
+
+## Fields
+
+- `question_id`: unique case id
+- `image`: image path, relative to this folder
+- `text`: question
+- `answer`, `answers`: primary gold answer, all gold answers
+- `category`, `level`: sweep and its value for this case
+- `evaluator`: scoring method (ANLS for DocVQA, relaxed accuracy for ChartQA, VQA soft accuracy for TextVQA,
+  exact match for GQA, option match for AI2D)
+- `source`: original dataset, split, question id and file paths within the original dataset release
+- `workload`: visual, prompt and answer token counts, and image size