Linear Attention Architectures: Mechanisms, Trade-offs, and Cross-Layer Routing

arXiv:2607.07953v1 Announce Type: new Abstract: Self-attention lets each token retrieve information from the full context, but its quadratic cost in sequence length limits training and inference at long context. This paper presents a comparative study of softmax attention and four recent recurrent linear-attention architectures: DeltaNet, Gated DeltaNet, Kimi Delta Attention, and Gated DeltaNet-2. We express these mechanisms in a common recurrent-memory notation, making explicit how they differ in expressivity, memory decay, erase and write control, training throughput, and implementation comp

Source: arXiv cs.LG — read the full report at the original publisher.

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