57 citations · 190 across the 32 of their papers we have counts for
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cs.IR2026
The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers
Zhe Xu, Prachi Agrawal, Kavosh Asadi +17
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 b…
cs.IR2026
Tokenizing Numerical and Embedding Features for LLM RecSys
Zhe Xu, Ankit Peshin, Chiyu Zhang +7
Large language models (LLMs) are increasingly used as backbone architectures for recommender systems because of their strong sequence modeling and representation learning capabilit…
cs.CL2026
LatentMT: Machine Translation with Latent Reasoning
Wei-Rui Chen, Samar M. Magdy, Chiyu Zhang +3
Latent-reasoning looped language models (LoopLMs) offer a different scaling path for machine translation (MT): instead of increasing parameter count or emitting explicit chain-of-t…