Update README.md

README.md

@@ -137,9 +137,6 @@ at load time.
 | 28 | `disk_4` | hanoi |
 | 29 | `disk_5` | hanoi |
 
-Note `background` appears twice — chess id 8 and hanoi id 23 are distinct
-classes in this unified namespace — so `segmentation_labels` names should be
-read together with `puzzle`. Per-class loss weights live in `classes.yaml`.
 
 ## pVQA answer spaces
 
@@ -158,30 +155,7 @@ read together with `puzzle`. Per-class loss weights live in `classes.yaml`.
 | nqueens | `q3` | Is the leftmost queen above or below the rightmost queen? | `[above, below]` | 100 |
 | nqueens | `q4` | Is the leftmost queen in the top, middle, or bottom third of the board? | `[top, middle, bottom]` | 100 |
 
-Answer spaces are not uniform: 2-way, 3-way, 4-way all occur, with `chess/q1`, `chess/q2` and `nqueens/q4` non-binary.
-That is why `options` is a column rather than an assumed `[Yes, No]`.
-
-Questions are deliberately coordinate-free ("the leftmost queen", not "the queen
-at row 4"), so a model must locate the referenced piece before it can answer.
-Full specs, including how each answer was derived from the board, are in
-`pvqa_questions.yaml`.
-
-## Notes and caveats
-
-- **The two tasks do not share images.** pVQA chess boards are separately
-  rendered, not the segmentation chess images, and N-Queens is not part of the
-  30-class label space. No image currently carries both a mask and a question.
-- **`image_path` repeats for N-Queens pVQA.** Each of the 100 boards is asked 4
-  questions, so 4 rows share an `image_path` with distinct `id`s. Chess pVQA asks
-  one question per board.
-- **Published N-Queens images are a derived render.** The sources are 1600×1600
-  JPEG, downscaled here to 512×512 with `PIL` `thumbnail(..., BILINEAR)` — the
-  same call the evaluation code applies, so these are the pixels models actually
-  saw. The originals remain the reference copy in the project repository.
-- Segmentation splits are sampled from the source datasets with a fixed seed
-  (`42`) and are reproducible.
-- `manifest.csv` maps `unified_id` to source task and split; `classes.yaml`
-  carries the class map and loss weights.
+
 
 ## Files