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FactorDrive redefines autonomous driving by seamlessly integrating spatial-physical evidence into adaptive reasoning, achieving state-of-the-art planning performance.
Fine-tuned robotic policies can be biased towards certain instruction factors, but a new bias-aware data strategy can significantly enhance their performance with fewer demonstrations.
SpanUQ reveals that span-level uncertainty quantification can enhance both the accuracy and efficiency of LLM outputs, outperforming existing methods by a factor of 10-20x.
Transforming failures into focused training tasks boosts tool-using language model performance by over 8% on key benchmarks.
