arXiv:2602.03681v2 Announce Type: replace Abstract: The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios. In contrast, linear attention model families provide a promising direction towards a more efficient sequential model. These linear attention models compress past KV values into a single hidden state, thereby efficiently reducing complexity during both training and inference. However, their expressivity remains limited by the size of their hidden state. Previous work proposed interleaving softmax and linear attention layers to redu
The quadratic computational complexity of foundational AI models is becoming a critical bottleneck, driving active research into more efficient architectures like linear attention models.
Improving the efficiency of AI models is crucial for scaling AI capabilities, enabling longer context windows, and reducing the compute and energy footprint of advanced AI systems.
New architectural approaches are emerging that could significantly enhance the scalability and efficiency of language models, offering alternatives to the prevailing transformer designs.
- · AI compute infrastructure providers
- · AI accelerator developers
- · Large language model developers
- · AI research institutions
- · Inefficient AI model architectures
- · Legacy compute infrastructure solely optimized for quadratic attention
More efficient AI models can process larger contexts, leading to more sophisticated and capable AI agents.
Reduced computational demands could democratize access to advanced AI development, fostering innovation beyond well-resourced labs.
Energy and compute savings from these architectural advancements could alleviate bottlenecks in the overall AI supply chain and reduce environmental impact.
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Read at arXiv cs.CL