Link DistillPath paper on arXiv

README.md

@@ -17,7 +17,7 @@ datasets:
 
 A 22M ViT-S/16 pathology tile encoder distilled from [H-optimus-0](https://huggingface.co/bioptimus/H-optimus-0) (1.1B ViT-g/14) into the [kaiko ViT-S/16](https://huggingface.co/1aurent/vit_small_patch16_224.kaiko_ai_towards_large_pathology_fms) student using backbone-token distillation on 6,000 public TCGA slides.
 
-**Paper:** [DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance](https://github.com/RamonKaspar/DistillPath)
+**Paper:** [DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance](https://arxiv.org/abs/2608.17872)
 Ramon Kaspar, Andrey Ignatov, Valentina Boeva. ETH Zurich.
 Published at the ECCV 2026 Workshop on Medical Foundation Models and Benchmarks (MedFM-Bench).