This paper introduces Complex KDA (CKDA), an enhancement to Kimi Delta Attention that improves the expressivity of linear RNNs while maintaining efficiency. By extending parameter ranges and combining delta-rule transformations with channel-wise gating, CKDA can model 2D rotations and track finite groups, achieving competitive performance on length extrapolation and language modeling tasks compared to Transformers and other linear RNNs.