Drop "(NeurIPS 2026)" from card + note Apple WBM reimplementation (Beyond Sensor Data, arXiv 2507.00191)

README.md

@@ -10,7 +10,7 @@ tags:
 # OpenMHC Outcome Prediction — WBM
 
 Track 1 (outcome prediction) reference checkpoint for the **MyHeartCounts /
-OpenMHC** wearable-health benchmark (NeurIPS 2026).
+OpenMHC** wearable-health benchmark.
 
 **This checkpoint is the WBM encoder** — a bi-directional Mamba2 contrastive
 self-supervised model that maps a week of wearable sensor data (168 hourly steps,
@@ -18,6 +18,10 @@ self-supervised model that maps a week of wearable sensor data (168 hourly steps
 encoder (per-user pooled → PCA-50 → linear probe) with a Linear fallback for
 users without a weekly embedding.
 
+This is an OpenMHC reimplementation of Apple's **WBM (Wearable Behavior Model)**,
+introduced in *Beyond Sensor Data: Foundation Models of Behavioral Data from
+Wearables Improve Health Predictions* (Apple, 2025; see references below).
+
 **Pretrained** with a contrastive objective on the MHC training split.
 
 - **Checkpoint format:** PyTorch Lightning checkpoint (`model.ckpt`) +
@@ -26,6 +30,11 @@ users without a weekly embedding.
 - **Outcome-prediction tasks:** 33 health & behavior labels (classification,
   ordinal, regression).
 
+## Model & implementation
+
+- [Paper: *Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions* (Apple, 2025)](https://arxiv.org/abs/2507.00191)
+- [Apple Machine Learning Research summary](https://machinelearning.apple.com/research/beyond-sensor)
+
 ## Requirements
 
 Running the encoder needs the CUDA-only Mamba2 kernels (`mamba-ssm`) and a GPU.