Complex KDA: Understanding and Enhancing the Expressivity of Kimi Delta Attention

Abstract: Linear RNNs based on the delta-rule enable efficient sequence modeling, but their linear updates with a low-rank correction constrain their expressivity. Prior work has shown that composing two delta-rule transitions in a single recurrent update can model a 2D rotation, but this increases the rank and the cost of the updates compared to a single transition. We show that Kimi Delta Attention (KDA) can realize 2D rotations by combining a single delta-rule transformation with a second reflection supplied by its channel-wise gate. This requires extending the parameter ranges of KDA by combining two existing range extensions: allowing gates in $[-1,1]$ and the delta-rule coefficient $β$ in $[0,2]$. We call the resulting model Complex KDA (CKDA). It preserves KDA's stability and efficiency, with transitions that remain diagonal-plus-rank-one and non-expansive, while reaching the state-tracking expressivity of DeltaProduct$_2$. We characterize the expressivity of CKDA and prove that every orthogonal diagonal-plus-rank-one matrix is exactly a CKDA transition matrix. A single CKDA layer can track every finite group isomorphic to a subgroup of $\mathrm{SO}(3)$, and many state-tracking results use one fewer layer for CKDA compared to other diagonal-plus-rank-one Linear RNNs. Empirically, combining both extensions yields the strongest length extrapolation among tested KDA range settings on $S_3$, $S_4$, and periodic audio continuation. In language modeling, CKDA outperforms Transformers and other linear RNNs, obtains similar results to a KDA baseline, and shows promising scaling behavior. Our code is open source at https://github.com/OpenEuroLLM/ComplexKDA and our models are available at https://huggingface.co/collections/openeurollm/complexkda.

• The paper introduces CKDA, extending the gate range to $[-1,1] and $eta$ to $[0,2]$ within single-layer Kimi Delta Attention.
• CKDA dynamically creates two reflections, one reflective gating and $eta = 2$ to allow a single-layer KDA head to simulate planar rotations and transitions as tracked by the system..
• CKDA alters group tracking abilities for noncommutative transformations, enabling accurate anticipation of group behaviors such as finite word extras, automata simulation, symbolic redundancy and real-world examples such as language prediction and periodic audio processes.

Motivation and central contribution

“Complex KDA: Understanding and Enhancing the Expressivity of Kimi Delta Attention” (2609.24797) studies the expressive limitations of linear recurrent models whose state transitions combine diagonal gating with a rank-one delta-rule update. The paper focuses on Kimi Delta Attention (KDA), whose transition for one head has the form

$$A_t = \left(I-\beta_t k_t k_t^\top\right)\operatorname{Diag}(\alpha_t),$$

where $k_t$ is a normalized key, $\beta_t$ controls the delta-rule update, and $\alpha_t$ is a channel-wise gate. Standard KDA uses nonnegative gates and typically restricts $\beta_t$ to $[0,1]$. These constraints preserve efficient chunkwise computation and non-expansive dynamics, but they also force the transition spectrum to remain real, preventing a single layer from representing persistent rotational dynamics.

The paper’s central proposal, Complex KDA (CKDA), combines two range extensions:

• signed channel-wise gates $\alpha_t\in[-1,1]^d$;
• extended delta-rule coefficients $\beta_t\in[0,2]$.

The combination is essential. Extending only the gate does not produce the required reflection geometry, while extending only $\beta$ leaves the transition spectrally real. Together, the two extensions allow a single diagonal-plus-rank-one transition to compose two reflections: one supplied by a signed diagonal gate and one supplied by the delta-rule term at $\beta=2$. Their noncommuting composition can be a planar rotation.

The resulting architecture retains KDA’s first-order real recurrence, fixed-size state, diagonal-plus-rank-one transition structure, non-expansiveness, and efficient implementation. The paper therefore distinguishes expressivity from merely increasing transition rank: CKDA attains state-tracking capabilities associated with two successive delta-rule updates without explicitly applying two rank-one updates per token.

The geometric mechanism is summarized by the planar construction below.

Figure 1: A signed diagonal reflection combined with a Householder reflection yields an arbitrary planar rotation within a single CKDA transition.

From scalar symmetry to signed channel-wise dynamics

The paper begins by comparing scalar-gated and channel-wise-gated delta transitions. In Gated DeltaNet, the gate is scalar, so the transition is

$k_t$0

The scalar factor commutes with the Householder-like delta update. Consequently, $k_t$1 is symmetric and has only real eigenvalues. Across multiple recurrent steps, scalar gates factor out as a global scale, so even signed scalar gates cannot provide an independently oriented transformation.

KDA’s channel-wise gate changes this algebra. For

$k_t$2

the two factors generally do not commute. However, the paper emphasizes that noncommutation alone is insufficient for complex eigenvalues. When all gate entries are nonnegative, the transition is similar to a symmetric matrix, and its spectrum remains real. A negative gate entry is required to make the diagonal factor indefinite.

In two dimensions, take a gate $k_t$3 and set $k_t$4. The resulting transition has the form

$k_t$5

where $k_t$6. Its eigenvalues become complex when

$k_t$7

This can occur only for $k_t$8. At $k_t$9, the diagonal gate is itself a coordinate reflection. Since the $\beta_t$0 delta update is a Householder reflection, the product becomes a rotation through angle $\beta_t$1. Varying the key direction therefore gives any planar rotation while maintaining unit spectral norm.

The paper’s stronger interpretation is that CKDA is not merely “complex” because it admits isolated complex eigenvalues. Its signed gate supplies a coordinate reflection that breaks the symmetry preventing rotational dynamics in ordinary KDA. The complex-conjugate eigenvalues are the spectral signature of this reflection composition.

Characterization of CKDA transitions

A principal theoretical result establishes that CKDA exactly contains the orthogonal diagonal-plus-rank-one family. Every orthogonal matrix of the form

$\beta_t$2

can be represented as

$\beta_t$3

where $\beta_t$4 is a diagonal sign matrix and $\beta_t$5. This is a signed Householder matrix and is a CKDA transition with $\beta_t$6 and gate entries in $\beta_t$7.

This characterization has two implications. First, CKDA captures all orthogonal transformations available within the diagonal-plus-rank-one class; adding an asymmetric rank-one correction does not enlarge the orthogonal family in the relevant sense. Second, the rank-one structure imposes a sharp spectral restriction: a single non-expansive CKDA transition can contain at most one non-real conjugate eigenvalue pair on the unit circle.

More generally, the paper decomposes a CKDA transition into a sign-magnitude component and a signed Householder component. The latter acts nontrivially in at most a two-dimensional subspace determined by the key’s positive and negative gate components. The remaining coordinates receive only real eigenvalues. Complex eigenvalues require all of the following:

1. at least one negative gate entry;
2. a key with support across coordinates having opposite gate signs;
3. $\beta_t$8.

At $\beta_t$9, the active two-dimensional block is an exact rotation. For intermediate $\alpha_t$0, it is a non-expansive contraction-rotation whose eigenvalues can move continuously from the real axis toward the unit circle.

The paper also proves factorization results for products of CKDA transitions. Any non-expansive $\alpha_t$1 matrix can be represented using at most $\alpha_t$2 CKDA factors, while any orthogonal matrix requires at most $\alpha_t$3 factors. The latter bound is sharp: an $\alpha_t$4-cycle permutation matrix cannot be expressed using fewer than $\alpha_t$5 diagonal-plus-rank-one factors. Thus CKDA does not make every matrix a single transition; rather, it maximizes the orthogonal expressivity available from one such transition and reduces the factor count for structured products.

State-tracking expressivity

The paper evaluates recurrent expressivity through state tracking, in which the model must compose input-dependent transformations over arbitrary sequence lengths. Group-word problems are particularly appropriate because they require order-sensitive, noncommutative composition rather than simple additive counting.

The main single-layer theorem states that one CKDA head can track every finite group isomorphic to a subgroup of $\alpha_t$6. Specifically, CKDA realizes:

• all finite cyclic groups $\alpha_t$7 and dihedral groups $\alpha_t$8 in two dimensions;
• $\alpha_t$9 and $\beta_t$0 in three dimensions;
• $\beta_t$1 in four dimensions using a many-to-one decoder.

The two-dimensional result follows directly from planar rotations and reflections. The $\beta_t$2 construction uses the rotational symmetry group of the cube. By conjugating the cube representation by a suitable global rotation, all relevant rotation axes can be placed in coordinate planes, satisfying the signed-Householder criterion.

The $\beta_t$3 result is more subtle. The icosahedral rotation group cannot be represented by signed Householder matrices in three dimensions: too many order-three and order-five rotation axes fail the coordinate-plane condition under every orientation. In four dimensions, however, the authors use the quaternionic relationship between $\beta_t$4 and $\beta_t$5 to encode each three-dimensional rotation as a signed Householder transition. A fixed decoder recovers the accumulated $\beta_t$6 rotation, although the hidden $\beta_t$7 state contains additional information. The resulting tracker has at most

$\beta_t$8

reachable hidden matrices for the $\beta_t$9 group elements.

This distinction between realization and tracking is important. A realization requires the hidden matrices themselves to form a faithful group representation. A tracker permits several hidden states to decode to the same target state. The four-dimensional $[0,1]$0 construction relies on the latter, and its representability does not imply easy optimization.

The paper also gives a negative result for $[0,1]$1. Under non-expansive transitions, finite reachability, and at most one persistent complex-conjugate pair per head, no single CKDA layer with any finite number of independent heads can track $[0,1]$2. The proof reduces finite affine tracking to a finite group of orthogonal transitions and constructs a commutator whose tenth power is the identity in every head, while its decoded permutation is a three-cycle whose tenth power is nonidentity. This obstruction applies despite arbitrary additive updates and many-to-one decoders.

The resulting separation is precise: CKDA handles $[0,1]$3 in one layer but not $[0,1]$4 under the stated assumptions. The obstruction is not simply dimensional; it follows from the spectral limitation of rank-one non-expansive transitions.

Multi-layer simulation of automata

The paper extends the single-layer results to general finite-state and weighted computations. Three CKDA layers suffice for every finite group-word problem. If expansive updates with $[0,1]$5 are permitted, three layers also suffice for every regular language and for weighted finite automata over $[0,1]$6 under polynomial-precision exact arithmetic.

The construction uses a clock-buffer-accumulator organization. The clock tracks position modulo a fixed period, the buffer stores a bounded block of recent inputs, and the accumulator applies a factorization of the corresponding block transition. CKDA’s planar rotation allows the clock to be implemented in one layer. This removes one clock layer relative to constructions based on scalar-gated DeltaNet, yielding a three-layer rather than four-layer simulation.

The result depends on explicit arithmetic assumptions. The constructive proofs use exact arithmetic over a fixed real algebraic number field with polynomially bounded rational coefficients. They do not establish equivalent guarantees for ordinary floating-point execution. The WFA construction also requires $[0,1]$7, which sacrifices the non-expansiveness guarantee central to the stable CKDA regime.

Empirical evaluation on symbolic and periodic tasks

Group-word extrapolation

The authors train one-layer models on $[0,1]$8, $[0,1]$9, and $\alpha_t\in[-1,1]^d$0 word problems, with training lengths up to $\alpha_t\in[-1,1]^d$1 and evaluation at longer lengths. Among the tested KDA parameter ranges, only the joint extension to signed gates and $\alpha_t\in[-1,1]^d$2 consistently provides strong extrapolation on $\alpha_t\in[-1,1]^d$3 and $\alpha_t\in[-1,1]^d$4S_3$\alpha_t\in[-1,1]^d$50.2$$, approximately the performance expected from distinguishing only the two parity-like cosets.</p> <p>The learned CKDA model recovers the theoretical mechanism rather than merely exploiting an unidentified parameter regime. A successful head approaches$$\alpha_t\in[-1,1]^d$$6, its gate becomes nearly sign-valued, and its transition spectrum develops a complex-conjugate pair near the unit circle.</p> <p><img src="https://images.emergentmind.com/paper-images/2609-24797/s3_signed_kda_interpretability.png" alt="Figure 2" title="" class="markdown-image" loading="lazy"></p> <p><p class="figure-caption">Figure 2: Learned$$\alpha_t\in[-1,1]^d$7 transitions use near-reflection updates, nearly binary signed gates, low-dimensional key structure, and complex eigenvalues.

The $\alpha_t\in[-1,1]^d$8 construction was not learned from standard random initialization. It became learnable under a theory-informed quaternion initialization, with a modified architecture and training schedule. This result supports the representability theorem but simultaneously demonstrates that representational capacity and optimization accessibility are distinct.

Periodic waveform continuation

The periodic audio experiment tests whether complex modes support phase preservation beyond the training horizon. A single recurrent layer observes a half-bar cue and must continue a periodic waveform after the input becomes zero. Models are trained up to sequence length $\alpha_t\in[-1,1]^d$9 and evaluated through length $\beta_t\in[0,2]$0.

At length $\beta_t\in[0,2]$$1, CKDA achieves a reported <a href="https://www.emergentmind.com/topics/signal-to-noise-ratio-snr" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">signal-to-noise ratio</a> of <strong>38.1 dB</strong>, whereas the causal Transformer falls to <strong>2.8 dB</strong>. The other KDA range configurations fail to maintain accurate phase over the same extrapolation horizon. This is direct evidence that the rotational mechanism learned in symbolic tasks can also support continuous periodic continuation.</p> <p>The result is not uniformly favorable to CKDA: a <a href="https://www.emergentmind.com/topics/gated-recurrent-unit-gru-block" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">GRU</a> achieves the lowest waveform error. Hence the experiment isolates the value of stable linear rotational dynamics for extrapolation, not overall superiority over nonlinear recurrent models.</p> <p><img src="https://images.emergentmind.com/paper-images/2609-24797/groove_waveform_main.png" alt="Figure 3" title="" class="markdown-image" loading="lazy"></p> <p><p class="figure-caption">Figure 3: CKDA maintains periodic waveform phase substantially beyond the maximum training length, although the GRU remains more accurate.</p></p> <h2 class='paper-heading' id='language-modeling-and-systems-results'>Language modeling and systems results</h2> <p>The language-modeling experiments assess whether the additional recurrence expressivity translates into improvements on natural-language objectives. The answer is qualified. CKDA generally performs on par with KDA, while both outperform the Transformer and several recurrent baselines in the reported parameter-matched settings.</p> <p>In the$$\beta_t\in[0,2]$2M-parameter Nemotron-CC experiments, the best CKDA configuration reaches 52.30% average downstream accuracy, compared with 51.32% for standard KDA. In the smaller FineWeb ablations, CKDA reaches 52.21%, compared with 51.85% for KDA without SiLU on keys. These differences are modest and configuration-dependent; CKDA does not consistently minimize validation perplexity.

At $\beta_t\in[0,2]$3B parameters trained on $\beta_t\in[0,2]$4B FineWeb-Edu tokens, the non-hybrid models report:

Model	WikiText perplexity	LAMBADA perplexity	Average accuracy
KDA, bounded gate	15.73	10.53	54.09%
CKDA	15.78	10.08	54.06%
Gated DeltaNet-2	15.90	11.41	53.11%
Mamba-3 MIMO	16.45	11.66	52.39%
Transformer hybrid baseline	19.22	13.72	50.86%

The central language-modeling claim is therefore not that CKDA decisively improves perplexity over KDA. Rather, CKDA preserves KDA-level language performance while adding a theoretically meaningful rotational mechanism and retaining almost all of KDA’s throughput. The implementation achieves approximately 96–97% of standard KDA throughput. The authors explicitly avoid claiming an inherent computational advantage over DeltaProduct$\beta_t\in[0,2]$5, whose measured throughput is competitive despite applying two delta-rule updates per token.

Scaling experiments across $\beta_t\in[0,2]$6M to $\beta_t\in[0,2]$7B parameters and $\beta_t\in[0,2]$8B to $\beta_t\in[0,2]$9B tokens show that CKDA and KDA consistently outperform the Transformer baseline in the reported grid. At $\beta$0B parameters and $\beta$1B tokens, CKDA obtains validation loss 2.1061, compared with 2.1630 for the Transformer and 2.1046 for KDA. The difference between CKDA and KDA is negligible at this scale, supporting the paper’s more restrained conclusion that CKDA has comparable scaling behavior rather than a clear scaling-law advantage.

Hybrid models produce a similar pattern. CKDA with a $\beta$2 recurrent-to-attention ratio obtains average downstream accuracy close to the corresponding KDA hybrid, with differences varying across evaluations. The experiments also show that complex transitions emerge during language-model training: negative gates appear most often in early layers, $\beta$3 occurs throughout the network, and complex eigenvalues are concentrated particularly in the first two layers.

Figure 4: In trained $\beta$4B CKDA models, negative gates, extended $\beta$5 values, and complex transition spectra emerge during optimization, especially in early layers.

The mechanistic interpretation remains incomplete. The paper identifies when the extended ranges are used but does not determine which computations they implement in language modeling.

Limitations and open questions

CKDA remains constrained by its rank-one transition structure. A non-expansive transition of this type supports at most one persistent complex-conjugate eigenvalue pair, so it cannot provide a full bank of independent oscillatory modes within one head. Products of CKDA transitions are more expressive, but the factor count increases with dimension.

The theoretical results also depend on assumptions that limit their direct interpretation for practical floating-point models. The constructive state-tracking theorems use exact finite datatypes or polynomial-precision arithmetic over fixed algebraic number fields. They do not provide error bounds for approximate rotations under arbitrarily long recurrent execution. The $\beta$6 impossibility theorem additionally assumes finite reachability, which is natural for the exact group constructions but does not automatically follow from finite-precision computation.

Representability does not imply learnability. Standard training failed to discover the $\beta$7 solution, and the successful experiment used theory-based initialization together with a different setup. The paper therefore leaves open whether generic optimization can reliably discover the relevant signed-reflection geometry.

The language-modeling evidence also does not establish a broad empirical advantage. CKDA matches KDA more closely than it surpasses it, and the main improvements over Transformer baselines may reflect the broader recurrent architecture, training recipe, or hybrid design rather than the signed-gate mechanism itself. The reported hybrid comparisons use different attention configurations from some baselines, and the authors acknowledge that this complicates causal attribution.

Finally, the functional roles of negative gates and $\beta$8 in trained LLMs remain unresolved. The paper observes their emergence but does not provide a mechanistic decomposition connecting individual complex transitions to specific long-context or linguistic computations.

Conclusion

The paper establishes that signed channel-wise gating fundamentally changes the expressive geometry of KDA. When combined with $\beta$9, it lets one non-expansive diagonal-plus-rank-one transition compose two reflections and realize arbitrary planar rotations. CKDA consequently captures the full orthogonal diagonal-plus-rank-one family, supports one-layer tracking of substantial nonabelian groups including $\beta=2$0 and $\beta=2$1, and reduces the layer count of several automata constructions.

The empirical results validate the mechanism most clearly on symbolic extrapolation and periodic waveform continuation: CKDA is the only tested KDA range configuration that reliably preserves the relevant long-horizon structure, achieving 38.1 dB SNR at length 264 in the waveform task. In language modeling, its primary result is parity with KDA rather than a decisive improvement, together with near-baseline throughput and evidence that the extended parameter ranges are used after training. The remaining central question is whether the theoretical rotational capacity can be made reliably learnable and translated into a reproducible advantage on workloads where long-horizon state composition is a limiting factor.