Add metadata front matter

README.md

@@ -1,3 +1,38 @@
+---
+library_name: pytorch
+license: mit
+pipeline_tag: other
+tags:
+  - arc-prize-2025
+  - program-synthesis
+  - tiny-recursive-models
+  - recursive-reasoning
+  - kaggle
+  - act
+datasets:
+  - arc-prize-2025
+model-index:
+  - name: Tiny Recursive Models — ARC-AGI-2 (Step 72,385)
+    results:
+      - task:
+          type: program-synthesis
+          name: ARC Prize 2025
+        dataset:
+          name: ARC Prize 2025 Public Evaluation
+          type: arc-prize-2025
+          split: evaluation
+        metrics:
+          - type: accuracy
+            name: Accuracy
+            value: 0.6283
+          - type: loss
+            name: LM Loss
+            value: 2.0186
+          - type: accuracy
+            name: Halt Accuracy
+            value: 0.9070
+---
+
 # Tiny Recursive Models — ARC-AGI-2 (8×GPU, Step 72,385)
 
 ## Model Summary
@@ -117,26 +152,3 @@ If you use this model, please cite the Tiny Recursive Models paper and the ARC P
 - **Data Privacy**: Training and evaluation use public ARC datasets; no personal data involved.
 
 ---
-
-```yaml
-model-index:
-  - name: Tiny Recursive Models — ARC-AGI-2 (Step 72,385)
-    results:
-      - task:
-          type: program-synthesis
-          name: ARC Prize 2025
-        dataset:
-          name: ARC Prize 2025 Public Evaluation
-          type: arc-prize-2025
-          split: evaluation
-        metrics:
-          - type: accuracy
-            name: Accuracy
-            value: 0.6283
-          - type: loss
-            name: LM Loss
-            value: 2.0186
-          - type: accuracy
-            name: Halt Accuracy
-            value: 0.9070
-```