Google claims the residual block that lets a layer attend over the outputs of several earlier layers, filed in Greece one Monday and posted to arXiv the next
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United States Patent Application Publication No. 2026/0330219 A1, Google LLC, "Attention Neural Networks With Generalized Residual Attention Blocks," Application No. 19/468,910, filed February 3, 2026, claiming priority to Greek Application No. 20250100075 filed February 3, 2025, published October 22, 2026
Claim 1 is a system running an attention neural network whose stack contains generalized residual attention blocks. Each one takes as its input the outputs of two or more preceding blocks rather than only the one just before it, generates a query vector from that input through a query generalized residual block with one or more sets of weights, and applies attention over the input using that query. The dependent claims give the key and value paths the same treatment (claims 8 and 9); spell out the block as layer normalization, a product with a first set of weights shared across the preceding blocks, a sum with a second set that has a subset per preceding block, and a ReLU activation (claims 3 through 6); name multi-head depth-wise cross-attention (claim 12); and let the input grow in dimensionality deeper in the stack (claim 16). The specification reports up to three times fewer training steps to reach comparable perplexity on language and image modeling, and says the first set of weights can start at zero so the block begins as an ordinary residual network. The six inventors posted "DeepCrossAttention: Supercharging Transformer Residual Connections" to arXiv on February 10, 2025, seven days after the Greek filing. As published, claim 2 depends from "the method of claim 1" although claim 1 is a system, and claim 7, which depends from claim 1, refers to "the combination," "the transformed product" and "the second set of weights"; the last two first appear in claims 2 and 3, and no claim in claim 7's chain introduces a combination.
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