VLA-World improves autonomous driving by using action-guided future image generation followed by reflective reasoning over the imagined scene to refine trajectories.
Visual instruction tuning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
MultiModalPFN extends TabPFN with modality projectors, a multi-head gated MLP, and cross-attention pooler to unify tabular and non-tabular inputs, outperforming prior methods on medical and general multimodal datasets.
citing papers explorer
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Learning Vision-Language-Action World Models for Autonomous Driving
VLA-World improves autonomous driving by using action-guided future image generation followed by reflective reasoning over the imagined scene to refine trajectories.
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MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning
MultiModalPFN extends TabPFN with modality projectors, a multi-head gated MLP, and cross-attention pooler to unify tabular and non-tabular inputs, outperforming prior methods on medical and general multimodal datasets.