update to TriProRep title + arXiv citation

README.md

@@ -11,26 +11,27 @@ tags:
 # triprorep-35M
 
 Structure-aware protein encoder, 35M parameters. ELECTRA-style corrective
-MLM pre-training on 83.6M ATLAS + PDB structures. The encoder reads three
-per-residue token streams (seq / bb / fa) and outputs a per-residue embedding
-of dimension `480` (fp16).
+MLM pre-training on protein structures. The encoder reads three per-residue
+token streams (seq / bb / fa) and outputs a per-residue embedding of
+dimension `480` (fp16).
 
 Architecture: `embed_dim=480`, `encoder_depth=10`, `encoder_heads=20`.
 
-Part of the [k-fold-structure release](https://github.com/<github-org>/k-fold-structure-release).
+Part of the [TriProRep release](https://github.com/hsjang0/TriProRep).
+Paper: [Atom-level Protein Representation Learning Improves Protein Structure Prediction](https://arxiv.org/abs/2605.22133) (arXiv:2605.22133).
 
 ## Files
 
 - `35M.ckpt`: full Lightning checkpoint.
 - `config.yaml`: model + data config. The path fields are placeholders, point them at your local data.
-- `backbone_tokenizer.pt`, `fullatom_tokenizer.pt`: structure tokenizers (PDB → token IDs). See Acknowledgements.
+- `backbone_tokenizer.pt`, `fullatom_tokenizer.pt`: structure tokenizers (PDB to token IDs). See Acknowledgements.
 
 ## Usage
 
 ```bash
 pip install torch huggingface_hub omegaconf numpy lmdb biotite
-git clone https://github.com/<github-org>/k-fold-structure-release.git
-cd k-fold-structure-release
+git clone https://github.com/hsjang0/TriProRep.git
+cd TriProRep
 ```
 
 ```python
@@ -44,7 +45,7 @@ print(features.shape)   # (L, 480) fp16
 ```
 
 `embed_pdb` downloads the bundled tokenizers from this repo on first call,
-then runs PDB → (seq, bb, fa) tokens → encoder. If you already have token
+then runs PDB to (seq, bb, fa) tokens to encoder. If you already have token
 IDs (e.g. from `k-fold-structure/repsp-triprorep-tokens`), call
 `encode(encoder, seq, bb, fa)` directly. For CPU, pass `device="cpu"` to
 `load_encoder`.
@@ -57,11 +58,14 @@ IDs (e.g. from `k-fold-structure/repsp-triprorep-tokens`), call
 ## Citation
 
 ```bibtex
-@misc{kfoldstructure,
-  title  = {K-Fold Structure: Structure-Aware Protein Encoders and a Per-Residue Representation Benchmark},
-  author = {<authors>},
+@misc{triprorep,
+  title  = {Atom-level Protein Representation Learning Improves Protein Structure Prediction},
+  author = {Kim, Taewon and Jang, Hyosoon and Seo, Hyunjin and Seo, Seonghwan and Kim, Hyeongwoo and Zhung, Wonho and Shin, Mingyeong and Kim, Wooyoun and Ahn, Sungsoo},
   year   = {2026},
-  url    = {https://huggingface.co/k-fold-structure}
+  eprint = {2605.22133},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.LG},
+  url    = {https://arxiv.org/abs/2605.22133}
 }
 ```