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University of Texas at Austin
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LLMs are not just tools; they are reshaping the very fabric of research, but they also introduce systemic risks that could undermine scientific integrity.
Achieve more reliable ADAS by using a physics-informed neural network that leverages damper characteristics to estimate wheel load more accurately than existing methods.
Achieve better video editing without retraining by dynamically locking background features based on a "hallucination metric" that detects when the diffusion model is about to go astray.
LLMs can boost autonomous driving behavior classification accuracy to over 94% by fusing numerical time-series data with high-level semantic features.
