Update README.md

README.md

@@ -15,23 +15,8 @@ GeoGramBench is a tailored benchmark dataset designed for evaluating the geometr
   - **Global Abstract Integration:** Complex problems requiring global spatial synthesis, parameterization, or 3D reasoning.
 - **Six Subtypes:** Problems span six mathematical subfields: `Angle`, `Length`, `Area`, `Volume`, `Ratio`, and `Count`, supporting fine-grained diagnostics.
 
-## Benchmark Highlights
-
-- GeoGramBench differs from traditional math benchmarks by emphasizing the symbolic-to-spatial abstraction capabilities of LLMs, leveraging procedural code expressed in formats such as `Asymptote`.
-- Initial evaluation using 17 state-of-the-art LLMs revealed substantial gaps, particularly for higher abstraction tasks:
-  - Models achieved less than **50%** accuracy on the most challenging **Global Abstract Integration** category.
-  - Even advanced models struggle to bridge procedural code with reliable spatial reasoning.
-
 ## Dataset Composition
 
-| Complexity Level           | Problem Count | Example Tasks                     |
-|----------------------------|---------------|-----------------------------------|
-| **Primitive Recognition**  | 102           | Compute the area of a triangle.  |
-| **Local Relation Composition** | 279       | Solve for angles in composite diagrams. |
-| **Global Abstract Integration** | 119       | Analyze 3D projections and symmetry. |
-
-### Subtype Distribution Across Levels
-
 | Subtype   | Primitive | Compositional | Abstract |
 |-----------|-----------|---------------|----------|
 | Angle     | 22        | 20            | 7        |
@@ -41,10 +26,40 @@ GeoGramBench is a tailored benchmark dataset designed for evaluating the geometr
 | Count     | 15        | 31            | 15       |
 | Volume    | 0         | 0             | 27       |
 
+## Benchmark Highlights
+
+- GeoGramBench differs from traditional math benchmarks by emphasizing the symbolic-to-spatial abstraction capabilities of LLMs, leveraging procedural code expressed in formats such as `Asymptote`.
+- Initial evaluation using 17 state-of-the-art LLMs revealed substantial gaps, particularly for higher abstraction tasks:
+  - Models achieved less than **50%** accuracy on the most challenging **Global Abstract Integration** category.
+  - Even advanced models struggle to bridge procedural code with reliable spatial reasoning.
+
+| Model | Primitive | Compositional | Abstract | ALL |
+|-------|-----------|-----------|-----------|--------------|
+| <strong>Closed-source Models</strong> |
+| GPT-o3-mini | 84.33 | 75.66 | 42.16 | 70.00 |
+| GPT-o1 | <strong>86.76</strong> | <strong>76.02</strong> | <strong>43.35</strong> | <strong>70.92</strong> |
+| GPT-o1-preview | 74.79 | 55.98 | 26.20 | 53.15 |
+| GPT-o1-mini | 79.62 | 63.21 | 29.09 | 58.94 |
+| GPT-4o | 39.81 | 21.29 | 4.96 | 21.40 |
+| Gemini-Pro-1.5 | 49.26 | 31.79 | 15.92 | 31.64 |
+| <strong>Open-source Models</strong> |
+| Qwen3-235B-Thinking-2507| <strong>89.09</strong> | <strong>79.12</strong> | <strong>49.05</strong> | <strong>74.00</strong> |
+| DeepSeek-R1 | 85.66 | 75.27 | 40.38 | 69.17 |
+| DeepSeek-v3-0324 | 80.57 | 68.89 | 27.67 | 62.05 |
+| QwQ-32B | 85.17 | 73.12 | 37.92 | 67.20 |
+| DeepSeek-R1-Distill-Qwen-32B | 79.78 | 67.83 | 35.92 | 62.68 |
+| Bespoke-Stratos-32B | 62.50 | 42.56 | 17.02 | 40.55 |
+| s1.1-32B | 75.37 | 58.96 | 26.58 | 54.60 |
+| DeepSeek-R1-Distill-Qwen-7B | 72.79 | 58.74 | 24.16 | 53.38 |
+| Sky-T1-mini-7B | 71.45 | 57.75 | 24.79 | 52.70 |
+| DeepSeek-R1-Distill-Qwen-1.5B | 60.29 | 39.02 | 11.03 | 36.70 |
+| DeepScaleR-1.5B-preview | 65.44 | 47.89 | 15.76 | 43.83 |
+
+
 ## Use Cases
 
 GeoGramBench is designed for:
-- Researchers developing **geometry-aware LLMs** or fine-tuning models for symbolic-to-spatial reasoning.
+- Researchers developing **geometry-aware LLMs** for symbolic-to-spatial reasoning.
 - Model diagnostics to pinpoint weaknesses in handling code-driven geometric reasoning or abstract spatial relations.
 - Evaluation and advancement of LLMs' performance on tasks involving spatial reasoning.