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Achieving over 90% success in robotic tasks with a model that can be trained on a single GPU challenges the notion that high performance requires massive computational resources.
Adaptive routing of perception priors allows PerceptDrive to generate optimal driving trajectories in real-time without complex post-processing.
Low-rank adaptation in vision-language alignment not only cuts costs but also boosts performance, revealing a surprising shift from hallucination to conservatism in model behavior.
Navigation success alone is a poor predictor of real-world web agent effectiveness, as revealed by the extensive evaluation of WebRetriever's diverse protocols.
