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The University of Sydney
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Scaling laws for contrastive learning reveal that learning interactions between views fundamentally alters optimization dynamics compared to linear regression.
Current LLM efficiency metrics fail to capture the true cost of tool use, as measured by wall-clock latency, but a new hardware-aware metric closes the gap.
Forget SVD: CARE aligns low-rank attention approximations with input activations, boosting accuracy up to 1.7x and slashing perplexity by 215x when converting models to multi-head latent attention.
