Update README.md

README.md

@@ -156,10 +156,10 @@ size_categories:
 
 - ✏️ **Core Task:** Given a real-world image in first-person perspective, a language instruction, and an embodiment type, models should predict a 2D navigation path in image space that solves the instruction.
 - 🤖 **Embodiments:** Four embodiment types capturing distinct physical and spatial constraints (human, legged robot, wheeled robot, or bicycle).
-- 📏 **Scale:** 1,000 diverse real-world scenarios and over 3,000 expert-annotated traces.
+- 📏 **Scale:** 1,002 diverse real-world scenarios and over 3,000 expert-annotated traces.
 - ⚖️ **Splits:**
-  - Validation split (50%) for model fine-tuning.
-  - Test split (50%) with hidden ground-truths for public leaderboard evaluation.
+  - Validation split (~50%) for model fine-tuning.
+  - Test split (~50%) with hidden ground-truths for public leaderboard evaluation.
 - 🔎 **Annotation Quality:** All images and traces manually collected and labeled by human experts.
 - 🏅 **Evaluation Metric:** Semantic-aware Trace Score, combining Dynamic Time Warping distance, goal endpoint error, and embodiment-conditioned semantic penalties.
 
@@ -198,5 +198,11 @@ If you find this dataset helpful for your work, please cite us with:
 **BibTeX:**
 
 ```
-TODO
+@article{Windecker2025NaviTrace,
+  author  = {Tim Windecker and Manthan Patel and Moritz Reuss and Richard Schwarzkopf and Cesar Cadena and Rudolf Lioutikov and Marco Hutter and Jonas Frey},
+  title   = {NaviTrace: Evaluating Embodied Navigation of Vision-Language Models},
+  year    = {2025},
+  month   = {October},
+  note    = {Awaiting peer review and journal submission.},
+}
 ```