| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Perceived Risk Prediction | Scenario MB | RMSE0.2391 | 81 | |
| Regression | Scenario IS2 | Size0 | 24 | |
| Classification | Scenario IS1 | Model Size0 | 24 | |
| Change point localization | Scenario 5 | Mismatch Proportion (K!=K)0.055 | 20 | |
| Change point localization | Scenario 3 | Error Proportion (K_hat != K)70.5 | 20 | |
| Policy Value Estimation | Scenario 4 | Policy Value Mean6.714 | 15 | |
| Policy Value Estimation | Scenario 3 | Policy Value (mean)1.879 | 15 | |
| Individualized Treatment Rule Estimation | Scenario 2 | Policy Value (PV)1.095 | 15 | |
| Individualized Treatment Rule Estimation | Scenario 1 | Policy Value (PV)1.017 | 15 | |
| Multi-agent trajectory planning | 10 agent scenario (ground truth goals) | Trajectory Success Rate2.31 | 12 | |
| Change point localization | Scenario 1 T=300 | Prop. K_hat != K1 | 10 | |
| Change point localization | Scenario 1 T=150 | Error Proportion0 | 10 | |
| Trajectory Tracking | Scenario A Out-of-Distribution 500 g | RMSE (Slow 0.5 m/s)0.301 | 8 | |
| Trajectory Tracking | Scenario A In-Distribution 300 g | RMSE (Slow 0.5 m/s)0.215 | 8 | |
| Economic Decision-Making | Scenario S3 Crisis Shock | Average Reward8.18 | 8 | |
| Constrained Motion Planning | Scenario 3 (two Franka Panda manipulators) 1.0 (test) | Success Rate100 | 8 | |
| Constrained motion planning | Scenario 2 (Two Franka Panda manipulators with closed-chain constraints) (test) | Success Rate100 | 8 | |
| Text-to-Image Generation | Scenario 4 | Similarity (95th Percentile)0.9262 | 8 | |
| Traffic Signal Control | Scenario VISSIM corridor 1 | ANP1,749.57 | 7 | |
| Simultaneous Exploration and Inspection | Scenario C | Finish Rate Avg98.7 | 7 | |
| Intent Recognition | Scenario Static S1 | Selection Accuracy98 | 6 | |
| Intent Recognition | Scenario 1 Dynamic | Tracking Rate92 | 6 | |
| Multi-robot motion planning | Scenario 3 Four-arm setup | Planning Time (Q1)0.075 | 6 | |
| Multi-robot motion planning | Scenario 2 Two-arm setup with obstacle | Time Q10.057 | 6 | |
| Multi-robot motion planning | Scenario 1 Two-arm setup | Planning Time (Q1)0.013 | 6 |