| title: Liquid Time Pocket | |
| emoji: 💧 | |
| colorFrom: cyan | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: "6.5.1" | |
| app_file: app.py | |
| pinned: false | |
| # Liquid Time Pocket | |
| Liquid Time Pocket trains a recurrent cell whose hidden state relaxes toward a | |
| learned candidate through per-unit continuous time constants. The GRU and plain | |
| RNN controls contain exactly the same 1,887 parameters and receive delta time as | |
| an ordinary input feature. | |
| All models train with gaps from 0.02 to 0.12, then face unseen gaps up to 0.40 on | |
| twice-longer sequences. The Space plots predictions against continuous timestamps. | |
| ## Verified local result | |
| At exactly 1,887 parameters, normal-gap RMSE was tightly matched: 0.0893 for the | |
| liquid cell, 0.0869 for the GRU, and 0.0881 for the RNN. Under unseen gaps up to | |
| 0.40, the liquid cell failed at 1.514 RMSE versus 0.274/0.281 for GRU/RNN. Its | |
| learned time constants ranged from 0.122 to 0.716, causing excessive phase-memory | |
| decay under the shifted interval distribution. | |
| ```bash | |
| uv run python projects/liquid-time-pocket/train.py | |
| uv run pytest tests/test_liquid_time_pocket.py | |
| ``` | |