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The document presents the Manifold-Geometric Transformer (MGT), a framework designed to address rank collapse in deep Transformer architectures by incorporating geometric constraints and dynamic learning mechanisms. It introduces manifold-constrained hyper-connections (mHC) to restrict updates to valid tangent space directions and deep delta learning (DDL) to allow for non-monotonic updates, enabling feature erasure. The authors argue that these innovations are crucial for scaling Transformers beyond curren

Author

theoshen8

Language

EN