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README.md

@@ -20,7 +20,7 @@ objects, switches devices on and off, moves the user between rooms, and asks whi
 unclear. It runs offline through llama.cpp and needs about 3 GB of GPU memory.
 
 On 499 instructions written by people for the ALFRED benchmark, it chose the right action on the right object 83.8% of
-the time, ahead of models with up to 120B parameters.
+the time, ahead of models with up to 120B parameters. The [article](https://huggingface.co/blog/ErenAta00/maverick-4b-unity-xr-agent) explains how it was trained and tested.
 
 ## Releases
 
@@ -28,6 +28,7 @@ the time, ahead of models with up to 120B parameters.
 |---|---|---|
 | [Maverick-4B-Unity-XR-Agent](https://huggingface.co/ErenAta00/Maverick-4B-Unity-XR-Agent-GGUF) | Model | Tool calling for Unity XR scenes, GGUF |
 | [Maverick XR Agent for Unity](https://huggingface.co/ErenAta00/Maverick-Unity) | Unity package | Connects the model to your scene; installs from the Package Manager |
+| [Offline Voice Control for Unity XR Scenes](https://huggingface.co/blog/ErenAta00/maverick-4b-unity-xr-agent) | Article | How Maverick was trained and tested |
 | [XRLab-SDK](https://github.com/mcbu-xrlab/XRLab-SDK) | Python SDK | Shows which vision-language models fit on an XR headset and what they cost to run |
 
 ## Links