Interdomain Attention: Beyond Token-Level Key-Value Memory
Read the original on arXiv Machine Learning →The paper introduces Interdomain Attention (IA), a hybrid architecture that blends softmax attention with recurrent memory within a single layer, leveraging a kernel regression perspective to compress keys and values into a state‑space model (SSM) memory. IA reconstructs the attention matrix implicitly from memory states, preserving the computational efficiency of SSMs while achieving performance gains. Experiments on models ranging from 125 M to 1.3 B parameters show IA surpasses an S4D baseline at matched memory size, outperforms standard Transformers on perplexity and common‑sense reasoning, and matches or exceeds SSM‑based models on genomic downstream tasks.
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