A utility-augmented Transformer that conditions attention projections on action–reward history outperforms feedback-blind baselines on non-stationary decision tasks.
A Survey of Autonomous Driving from a Deep Learning Perspective,
4 Pith papers cite this work, alongside 50 external citations. Polarity classification is still indexing.
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2026 4representative citing papers
Hardware-adapted Swin Transformer plus fixed-ratio GPU/DLA frame dispatch on Jetson Orin reaches ~126 FPS and ~24 ms DLA latency at 4 FPS/W with ~2% F1 loss.
LoCar is a localization-aware evaluation framework for in-vehicle assistants that identifies unstable Korean honorific control and weaker performance on strategic metrics like clarification and proactivity in current LLMs.
A tutorial that unifies explicit and implicit world models through shared predictive structure for applications in physical AI such as robotics.
citing papers explorer
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LoCar: Localization-Aware Evaluation of In-Vehicle Assistants through Fine-Grained Sociolinguistic Control
LoCar is a localization-aware evaluation framework for in-vehicle assistants that identifies unstable Korean honorific control and weaker performance on strategic metrics like clarification and proactivity in current LLMs.
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A Tutorial on World Models and Physical AI
A tutorial that unifies explicit and implicit world models through shared predictive structure for applications in physical AI such as robotics.