Add task category and paper link

README.md

@@ -1,20 +1,24 @@
 ---
 language:
-  - en
-tags:
-  - physics
-  - physical-simulation
-  - trajectory-prediction
-  - synthetic
-  - hdf5
+- en
 size_categories:
-  - 10K<n<100K
+- 10K<n<100K
+tags:
+- physics
+- physical-simulation
+- trajectory-prediction
+- synthetic
+- hdf5
+task_categories:
+- time-series-forecasting
 ---
 
 # HamiBalls
 
 HamiBalls is a pair of synthetic physical-trajectory datasets combining smooth spring dynamics with inelastic contacts. Each trajectory contains an initial state and 192 subsequent states, sampled at 30 Hz.
 
+**Paper:** [HamiFormer: Dual-Expert Diffusion Fields with Affine Symplectic Maps](https://huggingface.co/papers/2609.32838)
+
 [Code and generators](https://github.com/HamiFormer/HamiFormer) · [Model weights](https://huggingface.co/HamiFormer/HamiFormer-Assets)
 
 ## Environments
@@ -91,4 +95,4 @@ HDF5 slicing supports reading individual trajectories or batches without loading
 
 The code repository provides `generate-h1`, `pack-h1`, and `generate-h2` commands. Generator configurations are under [`configs/generator/`](https://github.com/HamiFormer/HamiFormer/tree/main/configs/generator); the supported simulator versions are Pymunk 7.3.0 and PyBullet 3.2.7.
 
-The datasets support research on multi-object dynamics, contact-aware prediction, and long-horizon physical simulation. Their physical systems and parameter ranges are defined by the accompanying generator configurations. The generator code is distributed under the [MIT license](https://github.com/HamiFormer/HamiFormer/blob/main/LICENSE).
+The datasets support research on multi-object dynamics, contact-aware prediction, and long-horizon physical simulation. Their physical systems and parameter ranges are defined by the accompanying generator configurations. The generator code is distributed under the [MIT license](https://github.com/HamiFormer/HamiFormer/blob/main/LICENSE).