arXiv:2607.07706v1 Announce Type: new Abstract: The quadratic cost of causal self-attention severely bottlenecks long-context transformer inference. While numerous post hoc linearization pipelines exist, it is difficult to identify which components preserve model quality. This work isolates the effect of state update design in a strict frozen-backbone regime. We show that softmax relies on key-dependent, rank-1 orthogonal projections, elucidating why delta-style networks outperform purely gated accumulation. We identify a potential source of approximation errors and introduce structural interv
The quadratic cost of transformer self-attention is a known bottleneck for long-context inference, and this research addresses that fundamental limitation by proposing a new analysis-driven linearization approach.
This research provides a deeper understanding of transformer linearization, potentially leading to more efficient and scalable AI models capable of processing much longer contexts, crucial for advanced AI agents and applications.
The focus shifts from trial-and-error linearization to analytically informed design, potentially accelerating the development of more performant and resource-efficient transformer architectures.
- · AI model developers
- · Cloud providers
- · AI research institutions
- · SaaS companies leveraging large language models
- · Inefficient AI architectures
- · Compute-limited AI applications
More efficient and longer-context transformer models become feasible, improving LLM capabilities.
This efficiency reduces the compute and energy burden of large AI models, lowering AI development and deployment costs.
Lower compute costs enable broader access to advanced AI, accelerating the adoption of complex AI agents and services across industries.
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Read at arXiv cs.LG