3 papers
cs.CL2026
Why Attend to Everything? Focus is the Key
Hengshuai Yao, Xing Chen, Ahmed Murtadha +8
Standard attention scales quadratically with sequence length. Efficient attention methods reduce this O(n^2) cost, but when retrofitted into pretrained models, they often degrade p…
cs.LG2026
GAIN: Multiplicative Modulation for Domain Adaptation
Hengshuai Yao, Xing Chen, Ahmed Murtadha +1
Adapting LLMs to new domains causes forgetting because standard methods (e.g., full fine-tuning, LoRA) inject new directions into the weight space. We show that forgetting is gover…
cs.LG2026
Thin Keys, Full Values: Reducing KV Cache via Low-Dimensional Attention Selection
Hengshuai Yao, Xing Chen, Ahmed Murtadha +1
Standard Transformer attention uses identical dimensionality for queries, keys, and values, yet these components serve different roles: queries and keys produce scalar attention we…