Update README with model/dataset documentation

README.md

@@ -53,40 +53,6 @@ You FIRST observe the image in <observe> </observe> tags, then visualise the rel
 Image size: {Width} x {Height}
 ```
 
-## Output Format
-
-The model generates structured output with four components:
-
-1. **`<observe>`**: Scene description covering relevant objects
-2. **`<scene>`**: JSON scene graph with objects (id, bbox) and relationships (subject, predicate, object)
-3. **`<think>`**: Step-by-step reasoning as internal monologue
-4. **`<answer>`**: Final answer with option letter and text
-
-### Example Output
-
-```
-<observe>
-The image shows a living room with a couch, a coffee table, and a cat sitting on the floor.
-</observe>
-<scene>
-{
-  "objects": [
-    {"id": "couch.1", "bbox": [50, 100, 400, 350]},
-    {"id": "cat.1", "bbox": [200, 300, 280, 400]},
-    {"id": "table.1", "bbox": [150, 250, 350, 320]}
-  ],
-  "relationships": [
-    {"subject": "cat.1", "predicate": "in front of", "object": "couch.1"},
-    {"subject": "cat.1", "predicate": "beside", "object": "table.1"}
-  ]
-}
-</scene>
-<think>
-Looking at the scene graph, the cat is positioned in front of the couch and beside the coffee table. The bounding box coordinates show the cat is at y=300-400 while the couch extends to y=350, confirming the cat is on the floor in front of the couch.
-</think>
-<answer> (B) in front of the couch </answer>
-```
-
 ## Usage
 
 ```python
@@ -129,19 +95,6 @@ output = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(output)
 ```
 
-## Evaluation Results
-
-SpatialThinker-7B achieves state-of-the-art performance on spatial reasoning benchmarks:
-
-| Benchmark | SpatialThinker-7B |
-|-----------|------------------------|
-| CV-Bench (3D) | Strong performance |
-| BLINK-Spatial | Outperforms GPT-4o |
-| SpatialBench | SOTA results |
-| RealWorldQA | Competitive |
-
-See the [paper](https://arxiv.org/abs/2511.07403) for detailed results.
-
 ## Citation
 
 ```bibtex