Update README.md

README.md

@@ -23,13 +23,6 @@ viewer: false
 A visual-question-answering benchmark of **synthetic astronomical figures** with
 exact, machine-generated ground truth.
 
-Every figure here was *drawn by us* with matplotlib, so for each one we know the
-things a reader of a paper can only estimate: the true minimum of the colour
-axis, the real angular width of the field, the exact axis limits, the colormap
-name, the distribution the data was drawn from. That is the point of the
-dataset — it measures whether a model can *read a scientific figure*, scored
-against generator truth rather than against another annotator's guess.
-
 Alongside the figures, the repo ships the **raw responses of three
 vision-language models** on a 150-figure subset, run twice: once on the clean
 figures and once on the same figures artificially aged to look like a scanned
@@ -48,7 +41,7 @@ page from a mid-century journal.
 | Images | 2,001 JPEG, RGB, ~2000–2700 px wide |
 | Model responses | 3 models x 150 figures x 2 conditions = **21,300** answered questions |
 | Ground truth | Generator-exact, not human-annotated |
-| Download size | ~8 GB of data; the full LFS history is considerably larger |
+| Download size | ~8 GB of data |
 | License | Apache 2.0 (see [Provenance](#provenance-and-attribution) for the sky cutouts) |
 
 ---
@@ -91,37 +84,29 @@ numbering and are not part of the 2,001.
 
 ## The figures
 
-Three categories, **667 of each** — the balance is deliberate, so accuracy in
-one category is never a function of how common it is.
+Three categories, **667 of each**:
 
 | Category | `plot0.type` | `plot0.distribution` | What it is |
 |---|---|---|---|
 | `contour` | `contour` | `random` / `linear` / `gmm` | A contour plot of synthetic data |
-| `sky-gmm` | `image of the sky` | `gmm` | A **synthetic** sky image: a Gaussian mixture, dressed in sky coordinates |
+| `sky-gmm` | `image of the sky` | `gmm` | A **synthetic** sky image: a Gaussian mixture, deposited to sky coordinates |
 | `sky-real` | `image of the sky` | `sky` | A **real** survey cutout, plotted the same way |
 
-The last two are the interesting pair: they are rendered identically and are
+The last two are rendered identically and are
 distinguishable only by the structure of the data itself. A model that answers
 the provenance question correctly has to tell a real astronomical field from a
 plausible-looking fake.
 
 Everything else about each figure is randomised independently — plot style,
 colormap, dpi, aspect ratio, font sizes, colorbar placement, tick formats,
-label text, coordinate epoch. No two figures share a look.
-
-### Figure ids carry no information
+label text, coordinate epoch. 
 
-Ids are shuffled. `vqa_000002` being a real cutout and `vqa_000001` being
-synthetic is an accident of ordering, and nothing in the filename encodes the
-category. This is intentional: the generator's own filenames *did* encode the
-real-vs-synthetic answer, which would have leaked the Level 3 ground truth to
-anything that saw a path.
 
 ---
 
 ## The questions
 
-Questions are graded into three levels by what a reader has to do to answer:
+Questions are organized by the conceptual level model developed by [Lundgard et. al 2022](https://arxiv.org/abs/2110.04406):
 
 | Level | What it asks for | Examples |
 |---|---|---|
@@ -129,8 +114,7 @@ Questions are graded into three levels by what a reader has to do to answer:
 | **Level 2** | Things requiring *estimation from the rendered data* | min/max/median/mean of the colour axis, angular field width and height |
 | **Level 3** | Things requiring *inference about how the data was made* | which distribution generated it; real sky vs. Gaussian mixture; pixel scale |
 
-Level 3 is the hard tier and the reason the dataset exists. Level 2 is where
-answers are most often wrong by orders of magnitude rather than by a little.
+Level 4 requires domain-specific expertise and is not covered by this dataset.
 
 ### Full question inventory