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[ARTICLE · art-59889] src=arxiv.org ↗ pub= topic=artificial-intelligence verified=true sentiment=· neutral

Semidirect Fourier Delta Attention: Phase-Controlled Delta Memory with Constructive Chunk-WY Kernels

Researchers introduced Semidirect Fourier Delta Attention (SFDA), a phase-controlled generalization of Kimi Delta Attention that replaces real diagonal decay with block-rotational Fourier control. The method achieves exact affine chunk transfer and formal stability bounds, and in toy state-tracking experiments, SFDA learned cyclic memory where the phase-disabled baseline remained near chance.

read1 min views1 publishedJul 15, 2026

arXiv:2607.11897v1 Announce Type: new Abstract: Linear attention replaces softmax attention's growing KV cache with a fixed recurrent state, but this compression limits exact state tracking and long-context memory. We introduce \emph{Semidirect Fourier Delta Attention} (SFDA), a phase-controlled generalization of Kimi Delta Attention that replaces real diagonal decay with block-rotational Fourier control: [ S_t=(I-\beta_t k_tk_t^)\Lambda_tS_{t-1}+\beta_tk_tv_t^, \qquad \Lambda_t=\diag(\alpha_t\odot e^{i\theta_t}). ] Our main result is a constructive chunk-WY factorization for products (A_t=\Lambda_t-u_tr_t^), giving [ A_t\cdots A_1=\Gamma_t-Y_tM_tW_t^ ] with rank growth bounded inside fixed chunks. This yields an exact affine chunk transfer, formal stability and complexity bounds, and a compact characterization of phase-plus-low-rank memory. We verify the algebra numerically and show in toy state-tracking experiments that SFDA learns cyclic memory where the phase-disabled KDA baseline remains near chance. Fused kernels and large-scale language-model comparisons are left to future work.

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