SPHERE
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Function Optimization | Sphere D=1000 | Final Value3.9387 | 19 | |
| High-dimensional optimization | Sphere D=10000 | Objective Value (Sphere D=10000)6.4338 | 13 | |
| Manifold Learning | Sphere | Spearman Correlation0.994 | 12 | |
| Manifold Learning | Sphere N=2,000, σ=0.05 | Trustworthiness99.6 | 12 | |
| Named Entity Recognition | SPHERE | CS NER Score80.47 | 12 | |
| Black-box Optimization | Sphere d=20 | Objective Value (Median)0.0032 | 9 | |
| Pose Graph Optimization | Sphere | Objective Value1.687 | 9 | |
| Classification | Sphere (test) | Accuracy100 | 9 | |
| Imputation | Sphere Synthetic | RMSE0.5682 | 8 | |
| Manifold Distance Correlation | Sphere | Spearman Correlation (row)0.994 | 7 | |
| Continuous Black-box Optimization | Sphere 30D | Average Objective Value0 | 7 | |
| Heat Equation Solving | Sphere analytic GT | NRMSE0 | 6 | |
| Mode recovery | Sphere Group B (d=64) | Mode Recovery Rate100 | 5 | |
| Mode recovery | Sphere Group B d=32 | Mode Recovery Rate100 | 5 | |
| Mode recovery | Sphere Group B (d=8) | Mode Recovery Rate100 | 5 | |
| Mode recovery | Sphere Group B (d=2) | Mode Recovery Rate100 | 5 | |
| Physical Parameter Identification | Sphere | Mass0.0785 | 5 | |
| Pose Graph Optimization | Sphere 3D | Objective Value1,687 | 5 | |
| Global Optimization | Sphere (closeness = 0.0001) | Average Cost518.14 | 5 | |
| Global Optimization | Sphere (test) | Distance to Global Optimum0 | 5 | |
| Relation Extraction | SPHERE Material Science | Hierarchical F181.91 | 5 | |
| Relation Extraction | SPHERE Biology | Rel+ F1 (Hierarchical)82.53 | 5 | |
| Relation Extraction | SPHERE Physics | Hierarchical Relation F176.93 | 5 | |
| Relation Extraction | SPHERE Computer Science | Rel+ F1 (Hierarchical)77.4 | 5 | |
| Intrinsic Dimension Estimation | Sphere d = 1 | Estimated Intrinsic Dimension1.94 | 4 |
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