4 papers
Blurry Window Attention
Axel Laborieux, Christos Sourmpis, Juan Gabriel Kostelec +1
The Softmax Attention operation in Transformer language models has a quadratic complexity in the sequence length and a growing state size in the form of KV cache, which becomes a b…
When Perplexity Lies: Generation-Focused Distillation of Hybrid Sequence Models
Juan Gabriel Kostelec, Xiang Wang, Axel Laborieux +2
Converting a pretrained Transformer into a more efficient hybrid model through distillation offers a promising approach to reducing inference costs. However, achieving high-quality…
AllMem: A Memory-centric Recipe for Efficient Long-context Modeling
Ziming Wang, Xiang Wang, Kailong Peng +5
Large Language Models (LLMs) encounter significant performance bottlenecks in long-sequence tasks due to the computational complexity and memory overhead inherent in the self-atten…
Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Enhanced Model Architectures
Parsa Omidi, Xingshuai Huang, Axel Laborieux +3
Memory is fundamental to intelligence, enabling learning, reasoning, and adaptability across biological and artificial systems. While Transformer architectures excel at sequence mo…