Dual-Branch Mamba Module

Updated 6 July 2026

Dual-Branch Mamba Module is a network design pattern using two complementary streams to separately model distinct dependency structures, enhancing both local detail and long-range dependencies.
One branch typically preserves modality-specific or local features while the other employs selective state-space modeling for efficient, linear-time long-range information capture.
Explicit fusion mechanisms—ranging from additive and gated operations to attention-based methods—are crucial for balancing branch outputs and optimizing performance.

Searching arXiv for papers on dual-branch Mamba modules and closely related variants. A Dual-Branch Mamba Module denotes a class of architectures that couple two complementary processing streams built around Mamba or Mamba-derived selective state-space models, usually to separate distinct dependency structures and then recombine them through explicit fusion. Across recent work, the term covers several recurring patterns: modality-separated branches for RGB and event streams in semantic segmentation, spatial–spectral branches for hyperspectral anomaly detection, channel–spatial streams for medical image segmentation, local–global or CNN–Mamba branches for restoration and detection, and temporal–spectral or local–global branches in graph and audio modeling. Despite this task diversity, the common design principle is stable: one branch preserves or enhances information that would otherwise be under-modeled by a single sequential scan, while the other exploits Mamba’s linear-time selective recurrence for long-range dependency modeling; fusion is then performed through additive, gated, attention-based, or residual mechanisms to balance complementary representations (Gu et al., 30 Dec 2025, Pant et al., 4 Feb 2026, Zheng et al., 20 Mar 2026, Duan et al., 30 Jun 2026).

1. Definition and design pattern

In the literature, “dual-branch” does not refer to a single canonical block, but to a family of modules in which two pathways are assigned different inductive biases and sequence axes. In MambaSeg, the dual branches are two parallel Vision Mamba encoders for RGB images and event streams, with fusion deferred to a Dual-Dimensional Interaction Module operating in space and time (Gu et al., 30 Dec 2025). In DMS2F-HAD, the branches are spatial and spectral, each scanning along the natural axis of the hyperspectral signal before adaptive gated fusion (Pant et al., 4 Feb 2026). In CS-MUNet, the broader architecture is channel–spatial dual-stream, with Channel Mamba State Aggregation at the bottleneck and a Boundary-Aware State Mamba decoder-side fusion module (Zheng et al., 20 Mar 2026). In RCL-Mamba, the duality is explicitly between an SSM/Mamba branch for large-scale blur trajectories and a CNN branch for local detail restoration (Duan et al., 30 Jun 2026).

This suggests that a Dual-Branch Mamba Module is best understood functionally rather than structurally. One branch typically addresses long-range, nonlocal, or axis-specific dependencies through selective state-space recurrence; the other preserves local geometry, boundary fidelity, spectral continuity, or modality-specific information through convolution, attention, or an alternative Mamba scan order. In some systems both branches are Mamba-based, as in dual VMamba encoders or dual-path temporal–spectral Mamba designs (Gu et al., 30 Dec 2025, Ma et al., 2 Sep 2025). In others, Mamba is paired with CNNs, Transformers, SNNs, or KANs to exploit complementary computational regimes (Duan et al., 30 Jun 2026, Sun et al., 2024, Fan et al., 4 Jun 2026, Yang et al., 24 May 2025).

A second recurrent property is axis specialization. The branch split often corresponds to a decomposition of the input domain: space versus time, spatial versus spectral, amplitude versus phase, or local versus global sequence order. This is explicit in ESTM, where SMamba scans frequency and TMamba scans time (Ma et al., 2 Sep 2025), and in BSDB-Net, where a Mamba-based module performs bidirectional frequency scan and unidirectional time scan after amplitude–phase decoupling (Fan et al., 2024). The implication is that dual-branch design is frequently used to preserve structured heterogeneity in the data rather than collapsing all dependencies into one homogeneous token sequence.

2. State-space foundations and why dual branches recur

The mathematical basis across these models is the state-space recurrence used by Mamba-like blocks. MambaSeg presents the canonical discrete form

$s_{t+1} = A s_t + B x_t,\quad y_t = C s_t + D x_t,$

and emphasizes input-conditioned selectivity such as $B=B(x_t)$ and $C=C(x_t)$ for dynamic filtering without quadratic attention (Gu et al., 30 Dec 2025). DMS2F-HAD uses the equivalent formulation

$x_t = A x_{t-1} + B u_t,\quad y_t = C x_t + D u_t,$

with hardware-aware selective scan and linear complexity in sequence length (Pant et al., 4 Feb 2026). Related continuous/discrete formulations with zero-order hold or learned discretization appear throughout CS-MUNet, DB-MSMUNet, RCL-Mamba, HybridMamba, and TK-Mamba (Zheng et al., 20 Mar 2026, Guan et al., 8 Jan 2026, Duan et al., 30 Jun 2026, Wu et al., 18 Sep 2025, Yang et al., 24 May 2025).

A central technical reason dual branches recur is that selective SSMs are linear in the chosen sequence length, but the choice of sequence axis is not neutral. Flattening a 2D or 3D tensor into one ordering privileges some dependencies and weakens others. VMamba-style SS2D partly addresses this by scanning multiple directions over images (Gu et al., 30 Dec 2025), while HybridMamba uses forward, reverse, and inter-slice traversals to restore 3D contextual coverage (Wu et al., 18 Sep 2025). Dual-branch designs generalize this idea: if one scan order or one representation cannot simultaneously preserve all salient structure, a second branch can model the omitted axis, modality, or frequency regime.

This also explains why dual-branch modules are often coupled to explicit fusion. Since each branch is selectively biased, the model must reconcile different latent semantics. Fusion therefore becomes a first-class operation rather than a simple residual sum. In MambaSeg, fusion is separated into cross-spatial and cross-temporal interaction modules (Gu et al., 30 Dec 2025). In DMS2F-HAD, a pixel-wise gate $G$ arbitrates between spatial and spectral representations,

$F_{\text{fusion}} = \mathrm{Proj}\!\left( G \odot F_{\text{spa}} + (1-G)\odot F_{\text{spe}} \right),$

thereby letting different regions favor different dependencies (Pant et al., 4 Feb 2026). In RCL-Mamba, a learned spatial gate balances Mamba-based global blur correction against CNN-based local repair (Duan et al., 30 Jun 2026). These designs indicate that the usefulness of dual branches depends not only on branch diversity but on controlled recombination.

3. Architectural variants across tasks

The empirical literature organizes Dual-Branch Mamba Modules into several major architectural families.

Family	Representative papers	Branch split
Multimodal parallel encoders	MambaSeg (Gu et al., 30 Dec 2025)	RGB branch + event branch
Axis-decoupled Mamba branches	DMS2F-HAD (Pant et al., 4 Feb 2026), ESTM (Ma et al., 2 Sep 2025)	spatial + spectral; temporal + spectral
Global–local hybrid branches	RCL-Mamba (Duan et al., 30 Jun 2026), CloudMamba (Yang et al., 8 Apr 2026), MDDFNet (Yu, 2 May 2025)	Mamba/global + CNN/local or fine-scale + large-scale
Channel–spatial or boundary-aware dual streams	CS-MUNet (Zheng et al., 20 Mar 2026), WDFFU-Mamba (Cai et al., 19 Dec 2025)	channel stream + spatial stream; channel attention + spatial attention
Modality- or representation-decoupled sequence models	BSDB-Net (Fan et al., 2024), DBHN-Net (Fan et al., 4 Jun 2026), TK-Mamba (Yang et al., 24 May 2025)	amplitude + phase; ANN + SNN; visual + text branches

In MambaSeg, the module is embedded in a four-stage encoder–decoder system. Two VMamba-T branches process image frames and event voxels in parallel, stage-wise channel/time alignment enforces compatible tensor shapes, and a Dual-Dimensional Interaction Module performs fusion at each scale before a SegFormer MLP head decodes fused multiscale features (Gu et al., 30 Dec 2025). The “dual-branch” concept here is tightly coupled to multimodal representation preservation.

DMS2F-HAD instead decouples a hyperspectral patch into a spatial branch with multi-scale feature extraction and a spectral branch with overlapping spectral grouping. Both branches use Mamba scan semantics, but along different axes: flattened spatial tokens for contextual anomaly structure, and grouped band sequences for per-pixel spectral signatures (Pant et al., 4 Feb 2026). The architecture is dual-branch not because modalities differ, but because the same HSI tensor contains incompatible dependency geometries.

CS-MUNet presents a more distributed interpretation. Channel Mamba State Aggregation redefines the channel dimension as the SSM sequence to model inter-channel anatomical semantics, while Boundary-Aware State Mamba operates on spatial sequences in decoder skip fusion and injects a boundary posterior directly into the Mamba scan parameters $\Delta$ and $B$ (Zheng et al., 20 Mar 2026). Here the duality is orthogonal rather than parallel at one layer: channel semantic refinement followed by spatial boundary-aware fusion.

RCL-Mamba and CloudMamba show another pattern: Mamba paired with a non-SSM branch to correct the weaknesses of either component. RCL-Mamba uses a Mamba-CNN dual-branch module inside a dual-domain restoration cascade, where the Mamba branch models large-scale anisotropic blur trajectories and the CNN branch restores local edges and textures (Duan et al., 30 Jun 2026). CloudMamba’s Dual-Scale Mamba block uses a fine-scale branch preserving local detail and a large-scale branch built from dilated convolutions before early fusion into a Mamba block (Yang et al., 8 Apr 2026). In both cases the second branch is not an alternative modality but an alternative receptive-field regime.

4. Fusion mechanisms and interaction operators

Fusion is the most technically diverse part of dual-branch Mamba design. Existing work uses several recurrent operator types.

MambaSeg’s Dual-Dimensional Interaction Module is among the most explicit formulations. Its Cross-Spatial Interaction Module computes shallow additive fusion, derives six pooled spatial maps from the two inputs and their sum, generates three attention maps by convolutions and sigmoid, applies cross-modal attention to event and image features, refines the fused map with SS2D, and then updates each modality with modality-aware residual attention (Gu et al., 30 Dec 2025). Its Cross-Temporal Interaction Module interleaves event and image features along the temporal axis, derives temporal attention weights from global max/average pooling and $1\times1$ convolutions, performs bidirectional temporal selective scan with S6 blocks, and updates both streams with temporal attention (Gu et al., 30 Dec 2025). This is a dual-branch fusion operator in which branch interaction occurs at both spatial and temporal levels.

DMS2F-HAD uses a simpler but highly explicit gate:

$G = \sigma(\mathrm{Conv}_{\text{gate}}([F_{\text{spa}},F_{\text{spe}}])),$

which leads to channel-wise and pixel-wise arbitration between branch outputs (Pant et al., 4 Feb 2026). The paper states that in homogeneous regions spectral coherence dominates, while in heterogeneous clutter spatial textures matter more, so the gate adapts branch contribution to local scene statistics (Pant et al., 4 Feb 2026).

CS-MUNet fuses its Boundary-Aware State Mamba branch and local SASF branch through an SE-style allocation:

$B=B(x_t)$ 0

followed by

$B=B(x_t)$ 1

The branch weights depend on pooled global summaries of both streams and the guidance map, tying branch selection to boundary evidence (Zheng et al., 20 Mar 2026).

WDFFU-Mamba’s Dual Attention Feature Fusion module separates channel and spatial attention. Channel attention is computed from global average and max pooling over low-resolution deep features, while spatial attention is computed from mean/max channel projections of high-resolution shallow features followed by a wavelet transform convolution. The fused output is

$B=B(x_t)$ 2

after which the result is channel-adjusted and upsampled (Cai et al., 19 Dec 2025). This is dual-branch fusion without two full encoder branches: one attention branch models channels from deep features, the other models spatial structure from shallow features.

Other papers replace explicit attention with concatenation plus reduction. MDDFNet’s Dynamic Dual Fusion concatenates outputs from four path-specific convolutions and compresses them by a fusion convolution, with dynamic routing and attention-like gating controlling relative path weights (Yu, 2 May 2025). TransMamba fuses Transformer and Mamba branch outputs at each encoder and decoder stage by channel-wise concatenation followed by channel reduction (Sun et al., 2024). Tmamba explicitly transfers positional information from Mamba to Transformer and channel attention from Transformer to Mamba via a T–M interaction structure, then performs cross-modal attention at the Transformer side (Zhu et al., 2024).

A plausible implication is that fusion design reflects the nature of branch asymmetry. When branches correspond to different modalities or axes with potential alignment ambiguity, fusion tends to be attention-based or gated. When the split is primarily receptive-field diversity or parallel feature extraction, concatenation plus lightweight gating often suffices.

5. Complexity, scaling, and efficiency rationale

A major motivation for Dual-Branch Mamba Modules is to retain long-range modeling while avoiding the quadratic cost of self-attention. MambaSeg explicitly contrasts $B=B(x_t)$ 3 selective SSM computation per scan with $B=B(x_t)$ 4 self-attention, noting benefits for high-resolution feature maps or long temporal windows (Gu et al., 30 Dec 2025). DMS2F-HAD states that Mamba scan complexity is $B=B(x_t)$ 5 in time and memory versus $B=B(x_t)$ 6 time and $B=B(x_t)$ 7 memory for Transformers, and reports average inference time across 14 datasets of $B=B(x_t)$ 8 versus $B=B(x_t)$ 9 for TDD and $C=C(x_t)$ 0 for GT-HAD, with approximately $C=C(x_t)$ 1 parameters and approximately $C=C(x_t)$ 2 FLOPs (Pant et al., 4 Feb 2026).

MambaSeg reports on DDD17 that MambaSeg has 25.44M parameters, 15.59G MACs, and 77.56% mIoU, compared with 34.39M parameters, 17.30G MACs, and 75.03% mIoU for EISNet (Gu et al., 30 Dec 2025). CloudMamba frames its Dual-Scale Mamba design against Transformer-based cloud detectors by emphasizing linear-time selective scan over four directional sequences instead of quadratic attention over all tokens (Yang et al., 8 Apr 2026). RCL-Mamba reports 17.2M parameters, 510.62 GFLOPs at 512×512, and approximately 250 ms inference time on RTX 4090, while sustaining reconstruction quality when reducing the number of views from 512 to 64 (Duan et al., 30 Jun 2026).

In speech and audio, the same efficiency argument appears in axis-decoupled settings. BSDB-Net reports that its Mamba-based dual-path network achieves an average 8.3 times reduction in computational complexity compared to baselines and a 25 times reduction compared to transformer-based models, with the “128-6” configuration at 1.68 G/s MACs and 9.78M parameters (Fan et al., 2024). DBHN-Net reports 1.32 G/s MACs and an average 7.5 fold reduction in computational complexity versus baselines, while outperforming LSTM- and Transformer-substituted variants of its TF-Mamba placement (Fan et al., 4 Jun 2026).

These results do not imply that dual-branch Mamba is uniformly cheaper than single-branch alternatives. Dual branches introduce duplicated projections, extra gating, and fusion layers. The efficiency benefit emerges because branch specialization lets each stream stay linear-time and modest-width rather than forcing a single expensive global operator to represent all dependency types. This suggests that dual-branch designs trade architectural duplication for axis-appropriate linear recurrence.

6. Empirical behavior, strengths, and limitations

Across domains, dual-branch Mamba designs are consistently associated with improvements on cases where one dependency type alone is insufficient. MambaSeg reports state-of-the-art performance on DDD17 and DSEC, with 77.56% mIoU and 96.33% pixel accuracy on DDD17 and 75.10% mIoU and 95.71% pixel accuracy on DSEC; ablations show that removing both CSIM and CTIM drops DDD17 mIoU to 74.38%, while using both recovers 77.56% (Gu et al., 30 Dec 2025). DMS2F-HAD reports an average AUC of 98.78% across fourteen hyperspectral benchmarks, with Gated Fusion outperforming Spatial Only, Spectral Only, and Addition Fusion (Pant et al., 4 Feb 2026). CS-MUNet reports mDice 86.16% and mIoU 79.16% on UW-Madison GI Tract and mDice 94.47% and mIoU 92.51% on WORD, with ablations showing drops when removing $C=C(x_t)$ 3 modulation or SE-style weight allocation (Zheng et al., 20 Mar 2026).

Medical segmentation papers repeatedly associate dual-branch mechanisms with improved boundaries. DB-MSMUNet attributes edge fidelity to its Edge Enhancement Path and small-structure recovery to its Multi-layer Decoder, while the Multi-scale Mamba Module supplies the dominant contextual gains (Guan et al., 8 Jan 2026). WDFFU-Mamba reports BUSI Dice 85.20±1.75% and HD95 17.90±4.38, and BUS Dice 84.48±2.89% and HD95 13.28±5.17, with WHF and DAFF together outperforming either alone (Cai et al., 19 Dec 2025). CloudMamba reports that its two-stage system with Dual-Scale Mamba plus uncertainty-guided refinement improves hard thin-cloud regions more than easy regions, while the early-fusion DS-Mamba variant outperforms separate-then-fuse ablations (Yang et al., 8 Apr 2026).

At the same time, several papers identify limitations. MambaSeg notes sensitivity to imperfect synchronization or calibration between event bins and image frames, dependence on event quality under sparse or noisy conditions, and scaling issues as temporal bin count $C=C(x_t)$ 4 increases (Gu et al., 30 Dec 2025). DMS2F-HAD notes sensitivity to spectral grouping hyperparameters $C=C(x_t)$ 5 and $C=C(x_t)$ 6, possible degradation under domain shifts and noise, and scalability challenges for very large scenes despite patching (Pant et al., 4 Feb 2026). CS-MUNet notes residual challenges on thin tubular structures in 2D and points toward 3D extensions (Zheng et al., 20 Mar 2026). CloudMamba identifies the heuristic nature of its uncertainty map and the modest average gains of the second stage relative to its extra overhead (Yang et al., 8 Apr 2026). RCL-Mamba notes that more physics-informed geometry mappings could further improve extreme low-view or high-noise regimes (Duan et al., 30 Jun 2026).

A recurring misconception is that dual-branch Mamba merely duplicates feature extractors and therefore adds redundancy. The empirical ablations argue against this. In ESTM, dual-branch STMamba with ESTgram achieves 95.76% average AUC versus 93.82% for SMamba and 93.94% for TMamba (Ma et al., 2 Sep 2025). In BSDB-Net, dual-branch performance exceeds MEN-only and CEN-only on PESQ, ESTOI, and SI-SDR (Fan et al., 2024). In DMbaGCN, removing either the local or global Mamba component degrades accuracy, with especially large losses without the local state-evolution path (He et al., 10 Nov 2025). The evidence suggests that branch diversity is not incidental but structurally necessary where the task contains heterogeneous dependency regimes.

7. Broader interpretation and reusable design principles

Taken together, the arXiv literature supports a general characterization of the Dual-Branch Mamba Module as a reusable systems pattern built from three elements: axis- or modality-specialized selective state modeling, explicit cross-branch interaction, and lightweight yet adaptive fusion. This pattern appears in vision segmentation (Gu et al., 30 Dec 2025), hyperspectral analysis (Pant et al., 4 Feb 2026), medical imaging (Zheng et al., 20 Mar 2026), graph representation learning (He et al., 10 Nov 2025), audio modeling (Ma et al., 2 Sep 2025), speech enhancement (Fan et al., 2024), and restoration (Duan et al., 30 Jun 2026).

Several reusable principles emerge. First, the branch split should follow a natural factorization of the data: modality, space/time, spatial/spectral, global/local, or semantic/detail. Second, Mamba is most effective when sequence formation respects domain geometry; many papers therefore pair Mamba with directional scans, grouped spectral windows, local windows, or geometry-aware transforms rather than naïve flattening (Gu et al., 30 Dec 2025, Pant et al., 4 Feb 2026, Duan et al., 30 Jun 2026, Wu et al., 18 Sep 2025). Third, fusion quality often determines whether branch complementarity translates into accuracy. Gated fusion, cross-dimensional interaction, and residual weight allocation repeatedly outperform naïve addition (Gu et al., 30 Dec 2025, Pant et al., 4 Feb 2026, Zheng et al., 20 Mar 2026). Fourth, dual-branch design is not limited to two Mamba branches; Mamba can be one branch within a broader hybrid system when another computational primitive contributes local bias, interpretability, or energy efficiency (Duan et al., 30 Jun 2026, Fan et al., 4 Jun 2026, Sun et al., 2024).

This suggests that “Dual-Branch Mamba Module” should be treated as a research motif rather than a fixed block. Its defining property is not a particular layer sequence, but the deliberate decomposition of structured dependencies into two coupled streams, at least one of which exploits selective state-space modeling to preserve long-range information at linear complexity.