BLUE trains a lightweight gate on frozen VLA hidden states to selectively activate language generation only when beneficial, achieving SOTA results with 2.54x inference speedup on driving benchmarks.
Fasionad: Fast and slow fusion thinking systems for human- like autonomous driving with adaptive feedback
3 Pith papers cite this work. Polarity classification is still indexing.
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SafeLens presents a fast-and-slow video guardrail framework that filters the SafeWatch dataset to 2.4% and adds Chain-of-Thought traces to achieve state-of-the-art moderation performance at reduced inference cost.
XEmbodied is a foundation model that integrates 3D geometric and physical signals into VLMs using a 3D Adapter and Efficient Image-Embodied Adapter, plus progressive curriculum and RL post-training, to improve spatial reasoning and embodied performance on 18 benchmarks.
citing papers explorer
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BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving
BLUE trains a lightweight gate on frozen VLA hidden states to selectively activate language generation only when beneficial, achieving SOTA results with 2.54x inference speedup on driving benchmarks.
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SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening
SafeLens presents a fast-and-slow video guardrail framework that filters the SafeWatch dataset to 2.4% and adds Chain-of-Thought traces to achieve state-of-the-art moderation performance at reduced inference cost.
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XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments
XEmbodied is a foundation model that integrates 3D geometric and physical signals into VLMs using a 3D Adapter and Efficient Image-Embodied Adapter, plus progressive curriculum and RL post-training, to improve spatial reasoning and embodied performance on 18 benchmarks.