Add pipeline tag, link to paper

README.md

@@ -1,70 +1,75 @@
----
-tags:
-  - robotics
-  - humanoid
-  - reinforcement-learning
-  - double-dqn
-  - pytorch
----
-
-# HumanoidTTT — Consolidation Policy
-
-Initial Double-DQN consolidation weights for
-[HumaniodTTT: Test-Time Capability Reuse for Efficient Humanoid Control](https://github.com/AIGeeksGroup/HumaniodTTT).
-
-## Model
-
-The policy scores retention actions for a finite-capacity capability store. Each
-action has a 71-dimensional consolidation feature and is scored by a shared
-`71 → 128 → 64 → 1` ReLU network with **17,537 parameters**. When the ten-slot store
-is full, the actions are `SKIP` or `REPLACE(j)`. Online Double-DQN updates use the
-fraction of subsequent requests with successful reuse between consecutive
-full-store qualified-miss decisions.
-
-## Release files
-
-| File | Contents |
-| --- | --- |
-| `consolidation_policy.pt` | Initial FP32 PyTorch scorer state dictionary, seed 83001. |
-| `config.json` | Architecture, feature layout, action mapping, online settings, and weight checksum. |
-| `SHA256SUMS` | File checksums. |
-
-This checkpoint is the starting point before online adaptation. It does not
-contain a prefilled capability store or optimizer/replay state.
-
-## Usage
-
-Install the code and download dependencies:
-
-```bash
-git clone https://github.com/AIGeeksGroup/HumaniodTTT.git
-cd HumaniodTTT
-pip install -e .
-pip install huggingface_hub
-```
-
-```python
-import torch
-from huggingface_hub import hf_hub_download
-from humanoid_ttt import ConsolidationPolicy
-
-path = hf_hub_download("AIGeeksGroup/HumanoidTTT", "consolidation_policy.pt")
-weights = torch.load(path, map_location="cpu", weights_only=True)
-policy = ConsolidationPolicy(weights, capacity=10, online_learning_rate=3e-5, gamma=0.95)
-```
-
-See the [GitHub README](https://github.com/AIGeeksGroup/HumaniodTTT#using-the-components)
-for entry applicability, execution feedback, and update interfaces. Pin the model
-revision and code commit when reproducing experiments.
-
-## Dependencies and scope
-
-The 45-dimensional A2 entry-applicability module uses state features and geometric
-certificates; it has no separate neural-network checkpoint. Applications supply
-motion-specific certificates, qualification evidence, and the execution runtime.
-Download frozen generation and tracking models from
-[OMG](https://github.com/Tsinghua-MARS-Lab/OMG) and
-[HoloMotion](https://github.com/HorizonRobotics/HoloMotion).
-
-The released scorer weights alone do not constitute an end-to-end robot controller.
-Successful loading verifies network compatibility, not complete experimental reproduction.
+---
+tags:
+- robotics
+- humanoid
+- reinforcement-learning
+- double-dqn
+- pytorch
+pipeline_tag: robotics
+---
+
+# HumanoidTTT — Consolidation Policy
+
+Initial Double-DQN consolidation weights for
+[HumanoidTTT: Test-Time Capability Reuse for Efficient Humanoid Control](https://huggingface.co/papers/2610.00198).
+
+- Paper: https://huggingface.co/papers/2610.00198
+- Project page: https://aigeeksgroup.github.io/HumanoidTTT
+- Code: https://github.com/AIGeeksGroup/HumanoidTTT
+
+## Model
+
+The policy scores retention actions for a finite-capacity capability store. Each
+action has a 71-dimensional consolidation feature and is scored by a shared
+`71 → 128 → 64 → 1` ReLU network with **17,537 parameters**. When the ten-slot store
+is full, the actions are `SKIP` or `REPLACE(j)`. Online Double-DQN updates use the
+fraction of subsequent requests with successful reuse between consecutive
+full-store qualified-miss decisions.
+
+## Release files
+
+| File | Contents |
+| --- | --- |
+| `consolidation_policy.pt` | Initial FP32 PyTorch scorer state dictionary, seed 83001. |
+| `config.json` | Architecture, feature layout, action mapping, online settings, and weight checksum. |
+| `SHA256SUMS` | File checksums. |
+
+This checkpoint is the starting point before online adaptation. It does not
+contain a prefilled capability store or optimizer/replay state.
+
+## Usage
+
+Install the code and download dependencies:
+
+```bash
+git clone https://github.com/AIGeeksGroup/HumanoidTTT.git
+cd HumanoidTTT
+pip install -e .
+pip install huggingface_hub
+```
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from humanoid_ttt import ConsolidationPolicy
+
+path = hf_hub_download("AIGeeksGroup/HumanoidTTT", "consolidation_policy.pt")
+weights = torch.load(path, map_location="cpu", weights_only=True)
+policy = ConsolidationPolicy(weights, capacity=10, online_learning_rate=3e-5, gamma=0.95)
+```
+
+See the [GitHub README](https://github.com/AIGeeksGroup/HumanoidTTT#using-the-components)
+for entry applicability, execution feedback, and update interfaces. Pin the model
+revision and code commit when reproducing experiments.
+
+## Dependencies and scope
+
+The 45-dimensional A2 entry-applicability module uses state features and geometric
+certificates; it has no separate neural-network checkpoint. Applications supply
+motion-specific certificates, qualification evidence, and the execution runtime.
+Download frozen generation and tracking models from
+[OMG](https://github.com/Tsinghua-MARS-Lab/OMG) and
+[HoloMotion](https://github.com/HorizonRobotics/HoloMotion).
+
+The released scorer weights alone do not constitute an end-to-end robot controller.
+Successful loading verifies network compatibility, not complete experimental reproduction.