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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2 Pith papers cite this work, alongside 50 external citations. Polarity classification is still indexing.
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Pith papers citing it
50
external citations · OpenAlex
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A tutorial that unifies explicit and implicit world models through shared predictive structure for applications in physical AI such as robotics.
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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.