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Mohamed bin Zayed University of Artificial Intelligence
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Compact retrievers can achieve nearly half the performance of larger models while being up to 9.7脳 faster, challenging the assumption that bigger is always better in retrieval tasks.
Transforming noisy web text into a high-quality petroleum engineering dataset boosts retrieval performance by over 44% on benchmark tasks.
Layer span variation can unlock a new dimension of deterministic rollout diversity, boosting performance by over 10 percentage points on reasoning tasks.
Forget retraining: HyperKKL uses a hypernetwork to dynamically condition KKL observers on exogenous inputs, enabling accurate state estimation for non-autonomous nonlinear systems.
