AI RESEARCH
Structured Multidimensional Representation Learning for Large Language Models
arXiv CS.CL
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ArXi:2603.05727v1 Announce Type: new Transformer architectures achieve state-of-the-art performance across a wide range of pattern recognition and natural language processing tasks, but their scaling is accompanied by substantial parameter growth and redundancy in the embedding dimension. In this work, we