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ReST shows that tailored Transformer architectures can outperform conventional models in industrial recommendation tasks, achieving significant gains in both accuracy and revenue.
OVIP-SG outperforms existing frameworks by preserving small object instances and enhancing retrieval accuracy, revolutionizing the mapping of fine-grained objects in 3D environments.
LaRec transforms LLM-based recommendations by enabling efficient exploration of diverse user interests through personalized latent reasoning paths.
