SlackDrive: Reclaiming Runtime Slack for Adaptive Driving Inference
cs.AI, cs.RO
Submitted: 2026-09-23
Updated: 2026-09-23
Terminology
Sources
- AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture-of-Transformers for End-to-End Autonomous Driving
- MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving
- BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving
- Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving
- Cross-Self KV Cache Pruning for Efficient Vision-Language Inference
- ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving
- DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving
- Q-Zoom: Query-Aware Adaptive Perception for Efficient Multimodal Large Language Models
- Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
- GigaWorld-Policy: An Efficient Action-Centered World--Action Model
- DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning
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