A new VLA model called SI uses a four-step chain-of-thought to derive driving intent and applies it via classifier-free guidance to a flow-matching trajectory generator, showing competitive Waymo scores and intent-controllable plans.
Advances in Neural Information Processing Systems , volume =
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
ReflectDrive-2 combines masked discrete diffusion with RL-aligned self-editing to generate and refine driving trajectories, reaching 91.0 PDMS on NAVSIM camera-only and 94.8 in best-of-6.
A context-aware synthetic augmentation framework with a hybrid clinical-language model improves psychological defense mechanism classification to 58.26% accuracy and 24.62% macro-F1 in low-resource conditions, outperforming the DMRS Co-Pilot baseline.
citing papers explorer
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Action Emergence from Streaming Intent
A new VLA model called SI uses a four-step chain-of-thought to derive driving intent and applies it via classifier-free guidance to a flow-matching trajectory generator, showing competitive Waymo scores and intent-controllable plans.
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ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving
ReflectDrive-2 combines masked discrete diffusion with RL-aligned self-editing to generate and refine driving trajectories, reaching 91.0 PDMS on NAVSIM camera-only and 94.8 in best-of-6.
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Mitigating Data Scarcity in Psychological Defense Classification with Context-Aware Synthetic Augmentation
A context-aware synthetic augmentation framework with a hybrid clinical-language model improves psychological defense mechanism classification to 58.26% accuracy and 24.62% macro-F1 in low-resource conditions, outperforming the DMRS Co-Pilot baseline.