Update README.md

README.md

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 # ShapeNetSDF
 
 Signed Distance Field (SDF) point samples derived from
-[ShapeNet Core](https://shapenet.org), for training and evaluating implicit
+[ShapeNet Core](#), for training and evaluating implicit
 neural representations / neural fields on 3D shapes.
 
+This dataset is shared as part of the CVPR 2026 paper [Weight Space Representation Learning via Neural Field Adaptaion](https://arxiv.org/abs/2512.01759) produced by `process_shapenet_to_sdf.py` from the. 
+Code for producing this dataset is shared in the [wsr.pytorch](https://github.com/IVRL/wsr.pytorch) neural-field codebase.
+
 Each shape is converted into a watertight manifold, normalized into the unit
 cube `[-1, 1]³`, and sampled into three point sets (`uniform`, `surface`,
 `groundtruth`), each stored as a `[N, 4]` `float32` array of `[x, y, z, sdf]`.
 
-This dataset is produced by `process_shapenet_to_sdf.py` from the
-[wsr.pytorch](https://github.com/IVRL/wsr.pytorch) neural-field codebase.
-
 ## Dataset structure
 
 The dataset is stored as **sharded Parquet** (one config per category, with