World Engine generates realistic safety-critical driving variations from logs for reinforcement post-training, reducing benchmark failures more than data scaling and showing collision reductions plus on-road gains in a production system.
Prediction of driver alertness levels on mountain roads using machine learning models: A naturalistic driving study in china
3 Pith papers cite this work. Polarity classification is still indexing.
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TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than TabPFN-2.5.
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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TabPFN-3: Technical Report
TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than TabPFN-2.5.