arXiv AI
Jul 23

AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally

arXiv:2607. 19363v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) is widely adopted in Transformers to encode positional information, yet standard implementations enforce a uniform frequency schedule and scaling across all attention heads.

By Shaowen Wang, Yuke Zheng, Tansheng Zhu, Shuang Chen, Shaofan Liu, Suncong Zheng, Jian Li
arXiv AI
Jun 8

RePo: Language Models with Context Re-Positioning

arXiv:2512. 14391v3 Announce Type: replace-cross Abstract: In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional indices.

By Huayang Li, Tianyu Zhao, Deng Cai, Richard Sproat