arXiv:2606.16093v1 Announce Type: new Abstract: Modeling long-range dependencies remains a central challenge in natural language processing. Transformer architectures achieve strong performance via self-attention but scale quadratically ($O(N^2)$) with sequence length, while State Space Models (SSMs) scale linearly ($O(N)$) but suffer from a selective recall bottleneck, struggling to retrieve precise information from compressed states. This creates a fundamental tradeoff between efficiency and perplexity. To tackle these challenges, we propose the \textit{Parallel Hybrid Architecture (PHA)}, w
The continuous push for larger context windows and more efficient AI models is driving the development of hybrid architectures to overcome current computational limitations.
This research addresses a critical bottleneck in large language model development, potentially enabling more powerful and contextually aware AI applications with reduced computational cost.
The proposed PHA architecture suggests a pathway to combine the strengths of Transformers and State Space Models, offering a potential solution for efficient long-context processing.
- · AI research institutions
- · NLP developers
- · Cloud computing providers
- · AI infrastructure companies
- · Companies reliant on solely quadratic-scaling models without optimization
- · Less efficient AI architectural approaches
Improved efficiency in processing long sequences of data for large language models.
Accelerated development of more sophisticated AI applications requiring deep contextual understanding.
Potentially democratizes access to advanced AI by reducing the computational barrier for large context models.
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Read at arXiv cs.CL