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Results show that out-of-domain generalization remains a major bottleneck and highlight the effectiveness of recent Arabic-specialized speech models, multimodal dialect identification approaches, and ensemble methods.
Experiments on three Amazon recommendation benchmarks show that soft-token fusion improves retrieval performance over LLM-based baselines, and that interaction-based fusion is more effective than direct concatenation of heterogeneous soft tokens.
Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains bottlenecked by prohibitive computational overhead and grounding challenges. In this paper, we revitalize the classic, highly efficient two-t...
LatentMT is introduced, the first systematic study of latent-reasoning LoopLMs for machine translation that adapts a small 2.6B-parameter backbone model with lightweight training and shows that hidden-representation differences shrink along the recurrent reasoning-step axis, supporting the observed saturation in perfor...
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