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README.md

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 ## Highlights
 
-- **First post-training framework for Physical AI**: Systematically addresses the long-tail safety-critical data scarcity problem in autonomous driving.
+- **A post-training framework for Physical AI**: Systematically addresses the long-tail safety-critical data scarcity problem in autonomous driving.
 - **Data-driven long-tail discovery**: Failure-prone scenarios are automatically identified from real-world driving logs by the pre-trained agent itself — no manual design, no synthetic perturbations.
 - **Photorealistic interactive simulation** via 3D Gaussian Splatting (3DGS): Each discovered scenario is reconstructed into a fully controllable, real-time-renderable simulation environment.
 - **Behavior-driven scenario generation**: Leverages Behavior World Model (BWM) to generalize and synthesize diverse traffic variations from long-tail scenarios, expanding sparse safety-critical events into a dense, learnable distribution.