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README.md

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+---
+license: other
+task_categories:
+- visual-question-answering
+tags:
+- benchability
+- figure4
+- vlm-sft
+---
+
+# BenchAbility Figure 4 -- `capability_guided`
+
+One of two training mixtures drawn from the **same** frozen 884,143-row candidate pool, with the
+**same** budget (60,000 intervention + 15,000 shared replay) and the same hyperparameters. The two
+differ only in how the samples are chosen, which is the whole experiment.
+
+| | |
+|---|---|
+| arm | `capability_guided` |
+| selection | by capability, gap-weighted from the Figure 3 scores |
+| intervention rows | 59,999 |
+| replay rows | 15,000 |
+| shards | 38 |
+| pool | 884,143 rows / 20 source datasets, capability-tagged sample-by-sample |
+
+## Columns
+
+| column | meaning |
+|---|---|
+| `uid` | `source-split-index`, stable across both arms |
+| `source` | original dataset (provenance) |
+| `capability` | BenchAbility leaf, assigned per sample by a vision-language classifier |
+| `split_role` | `intervention` or `replay` |
+| `question` / `answer` | the training turn; `<image>` marks where the image goes |
+| `image` | PNG bytes, embedded |
+
+`capability` is present in **both** arms so the mixtures can be compared, but the
+`source_uniform` draw never read it -- see below.
+
+## How this arm was drawn
+
+Per-capability quotas come from the measured Figure 3 scores:
+
+    gap(l)    = max(0, 95 - score(l))
+    weight(l) = 0.20/9 + 0.80 * gap(l) / sum(gap)
+
+The scores are measured on the diagnosis benchmarks through `scripts/build_fig1.py`'s own code path,
+not the placeholder table in the plan document. The target is 95 rather than 70 because the real
+scores top out at 93.69 -- at 70, seven of the nine capabilities have gap 0 and the experiment
+collapses into "train two capabilities".
+
+Within each capability bucket, samples are drawn **across benchmarks** under three diversity bounds:
+a 50% per-source cap, a 15% per-template cap, and at most 2 rows per image. Where a bound is
+arithmetically unsatisfiable it is relaxed one at a time and the relaxation is recorded --
+`key_information_extraction` is three templates in total by construction, and AI2D is 71% of
+`diagram_and_infographic_understanding`.
+
+Two capabilities cannot be filled from this pool: diagram (7,264 available against 11,081 requested)
+and KIE (1,416 against 1,864). The deficit is redistributed over the capabilities that have room, in
+gap proportion. It is **never** filled by upsampling with replacement, which would give those buckets
+more effective epochs than the same rows get in the other arm.
+
+## Reproducing
+
+```bash
+python fig4_training/pipeline/40_mix.py      # both arms from the frozen pool
+python fig4_training/pipeline/50_export.py   # this bundle
+```
+
+Full draw record, including every relaxed constraint and every shortfall, is in
+`mixture_manifest.json`.

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+}