Fix citation: correct title, full author list, OpenReview URL

README.md

@@ -15,7 +15,7 @@ pipeline_tag: zero-shot-image-classification
 
 SpatialWhisperer is a trimodal embedding model that aligns hematoxylin & eosin (H&E) image patches, gene-expression profiles, and free-text descriptions into a shared 2048-dimensional space. It enables zero-shot cell-type annotation of H&E patches and natural-language querying over histopathology and spatial transcriptomics data.
 
-This repository hosts the main checkpoint (seed=0) from the ICML 2026 paper *Trimodal Learning Enhances Zero-Shot Histopathology Annotation* (anonymized name `\ourmethod`).
+This repository hosts the main checkpoint (seed=0) from the ICML 2026 paper *[Transitive Representation Learning Enhances Histopathology Annotation](https://openreview.net/forum?id=Ze7U293Zw4)* (Schaefer et al., PMLR vol. 306).
 
 ## Model architecture
 
@@ -94,10 +94,16 @@ To compute image–text similarities for zero-shot cell-type annotation, encode
 
 ```bibtex
 @inproceedings{schaefer2026spatialwhisperer,
-  title  = {Trimodal Learning Enhances Zero-Shot Histopathology Annotation},
-  author = {Schaefer, Moritz and others},
-  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
-  year   = {2026},
+  title     = {Transitive Representation Learning Enhances Histopathology Annotation},
+  author    = {Schaefer, Moritz and Piran, Zoe and Walter, Nils Philipp and Awasthi, Animesh and Bock, Christoph and Leskovec, Jure and Good, Zinaida},
+  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
+  series    = {Proceedings of Machine Learning Research},
+  volume    = {306},
+  publisher = {PMLR},
+  address   = {Seoul, South Korea},
+  month     = jul,
+  year      = {2026},
+  url       = {https://openreview.net/forum?id=Ze7U293Zw4}
 }
 ```