Bottleneck Mamba Module: Design & Applications

• Bottleneck Mamba Module is a design that integrates channel reduction, a selective state-space (SS2D) core, and expansion with residual fusion to efficiently capture long-range interactions.
• It is applied across diverse architectures—from vision backbones to low-rank adapter setups—demonstrating versatility in computational and representational efficiency.
• The module addresses bottlenecks in channels, sequence length, and state dimensions, offering optimized trade-offs between model capacity, hardware efficiency, and performance.

A Bottleneck Mamba Module is a Mamba-based design pattern in which a selective state-space model is placed inside a constrained computational pathway—most explicitly a channel-reduction $\rightarrow$ SSM $\rightarrow$ channel-restoration block, but in related literature also at the network “neck,” in a low-rank adapter subspace, or between blocks as a sequence-length compression stage. The clearest explicit formulation appears in InceptionMamba’s GlobalMixer, which applies $1\times1$ reduction, SS2D, and $1\times1$ expansion with residual fusion (Wang et al., 10 Jun 2025). Closely related formulations include MambAdapter’s shared down-project $\rightarrow$ Mamba $\rightarrow$ up-project adapter in a low-rank subspace (Ali et al., 14 Jun 2026), while other works use “bottleneck” more topologically, for example at the deepest layer of an encoder–decoder or at a central multimodal fusion block (Ji et al., 2024, Xie et al., 27 May 2025). Across these variants, the common purpose is to insert Mamba’s linear-time long-range modeling where representational density is high and computational cost can be tightly controlled.

1. Canonical block structure

The canonical Bottleneck Mamba Module is the GlobalMixer of InceptionMamba. Given $X'\in\mathbb{R}^{B\times C\times H\times W}$ and bottleneck ratio $r=2$, the module is defined as

$$X^1 = \mathrm{Conv}_{1\times1}^{C\to C/r}(X'),\qquad X^2 = \mathrm{SS2D}(X^1),\qquad X^3 = \mathrm{Conv}_{1\times1}^{C/r\to C}(X^2),\qquad Y = X' + X^3.$$

The first projection reduces channels, the SS2D core performs a two-dimensional state-space scan, the second projection restores the original width, and the residual addition preserves block compatibility with the surrounding backbone (Wang et al., 10 Jun 2025). The paper states that this compresses channels before the expensive SSM, enlarges the receptive field to full image extent, and facilitates cross-channel interaction.

The same structural logic appears in MambAdapter, but in a low-rank adapter setting rather than a vision backbone. Each adapter projects $\hat X\in\mathbb{R}^{T\times d}$ to $\rightarrow$0 dimensions by a shared $\rightarrow$1, applies a lightweight Mamba block in that $\rightarrow$2-dimensional space, and projects back by the shared $\rightarrow$3. The adapter adds

$\rightarrow$4

with a per-layer scalar $\rightarrow$5 initialized to $\rightarrow$6 (Ali et al., 14 Jun 2026). Here the bottleneck is explicitly low-rank, and the Mamba block replaces the usual down-ReLU-up feed-forward neck.

Mamba-3 does not introduce a named Bottleneck Mamba Module, but its technical summary identifies a recurrent $\rightarrow$7 geometry in which each head reduces the $\rightarrow$8-dimensional token to an $\rightarrow$9-dimensional state via learnable projections and then projects back to $1\times1$0 (Lahoti et al., 16 Mar 2026). This suggests that “bottleneck” in Mamba systems can refer either to an explicit architectural block or to the internal state dimension on which the SSM operates.

2. Architectural placements and task-specific roles

The term is used across several architectures, but not always in the same sense.

Setting	Bottleneck form	Representative paper
Vision backbone	$1\times1$1 GlobalMixer with residual	InceptionMamba (Wang et al., 10 Jun 2025)
PETL in speech/audio	Shared down-project $1\times1$2 Mamba $1\times1$3 up-project adapter	MambAdapter (Ali et al., 14 Jun 2026)
Multimodal depth completion	Central Transformer–Mamba Bottleneck Fusion Module	HTMNet (Xie et al., 27 May 2025)
Semantic segmentation	Plug-and-play context head; not explicitly bottlenecked, but adaptable	G-Mamba (Chan et al., 7 Jun 2026)
MRI super-resolution	Bottleneck layer is MVC, explicitly not a Vision-Mamba block	Deform-Mamba (Ji et al., 2024)
Crack segmentation	Bottleneck-style residual module at low-resolution semantic layers	CrackMamba (He et al., 2024)

In HTMNet, the bottleneck is the central Bottleneck Fusion Module between a dual-branch encoder and a multi-scale fusion decoder. It receives deep multimodal features $1\times1$4 and repeats a sequential fusion block $1\times1$5 times by default:

$1\times1$6

followed by an MLP block with residual and LayerNorm (Xie et al., 27 May 2025). The bottleneck is therefore a network neck and a multimodal fusion locus rather than merely a channel compressor.

Deform-Mamba is an important counterexample. Its bottleneck is occupied by a multi-view context module that parallelly utilizes atrous convolution with different dilation rates, concatenates the resulting features, applies a $1\times1$7 projection, and adds a residual skip. The paper explicitly states that the bottleneck itself is not a Vision-Mamba block (Ji et al., 2024). This is significant because it distinguishes a “bottleneck in a Mamba network” from a “bottleneck Mamba module.”

CrackMamba occupies a middle ground. It is described as a residual feature-mixing module with two parallel attention-like branches plus a skip connection and is inserted in Stage 2 to Stage 5 of a U-shape segmentation backbone, preserving input and output shape $1\times1$8 (He et al., 2024). It behaves like a bottleneck block in placement and semantics, even though it does not use an explicit channel-reduction ratio.

3. Geometry-guided, attention-like, and multimodal variants

A major branch of the literature modifies the Mamba core by attaching structure-aware or attention-like control signals. In G-Mamba, the module is a drop-in replacement for ASPP, PPM, dual-attention, criss-cross attention, point-wise spatial attention, and object-contextual aggregation heads in six CNN segmentation models while preserving the $1\times1$9 tensor shape (Chan et al., 7 Jun 2026). Its geometry-guided selective scan computes

$1\times1$0

then feeds $1\times1$1 to uni-directional Mamba scans and fuses directional outputs through

$1\times1$2

The published text states that neither the single nor the cascade design explicitly uses a classic $1\times1$3 spatial $1\times1$4 bottleneck pattern, but also states that one could inject channel compression before the scan and expansion afterwards to form a true “bottleneck Mamba” block (Chan et al., 7 Jun 2026). This makes G-Mamba a direct blueprint for a bottlenecked geometry-guided module.

HTMNet’s Bottleneck Fusion Module combines self-attention and Mamba sequentially rather than replacing one with the other. The Self-Attention block models global pairwise interactions, whereas the subsequent Mamba block captures long-range sequential dependencies with linear complexity in token length (Xie et al., 27 May 2025). This hybridization is task-specific: the encoder branches carry RGB–D and depth features, and the bottleneck merges them once at the deepest level.

CrackMamba reinterprets Mamba through an attention perspective. One branch applies depth-wise Conv $1\times1$5 BatchNorm $1\times1$6 SiLU $1\times1$7 SS2D $1\times1$8 Sigmoid to generate a spatial attention map $1\times1$9, and a second branch uses two $\rightarrow$0 point-wise convolutions and Sigmoid to produce a channel attention map $\rightarrow$1. The module outputs

$\rightarrow$2

The paper’s stated interpretation is that wrapping Mamba’s dynamic $\rightarrow$3 into a Sigmoid gate and multiplying back into $\rightarrow$4 turns the Mamba branch into a spatial attention map (He et al., 2024).

4. Sequence and state bottlenecks beyond channels

The literature also uses bottleneck language for sequence length, state dimension, and gating pathways.

MTR is a training-free Mamba Token Reduction framework inserted immediately after each Mamba block. It computes a scalar importance score from the already available $\rightarrow$5 values, sorts tokens, splits them into Keep, Target, and Source groups, merges each Source token into its most-similar Target token by vector mean, and reorders the survivors back into their original sequence positions (Ma et al., 18 Jul 2025). The result is a true inter-block compression bottleneck that reduces sequence length from $\rightarrow$6 to approximately $\rightarrow$7. On ViM-B, it reduces FLOPs by approximately $\rightarrow$8 with only a $\rightarrow$9 drop in ImageNet performance without retraining (Ma et al., 18 Jul 2025).

DiffuApriel uses “bottleneck” in a compute sense. It replaces Transformer attention in masked diffusion LLMs with a bidirectional Mamba backbone:

$\rightarrow$0

with the two streams summed into a bidirectional representation (Singh et al., 19 Nov 2025). This removes the quadratic attention bottleneck and yields per-step complexity $\rightarrow$1 rather than $\rightarrow$2.

Mamba-3 identifies the internal state as a bottlenecked resource and improves its expressivity by exponential-trapezoidal discretization, complex-valued state updates, and a MIMO formulation. The technical summary states that Mamba-3 with $\rightarrow$3 matches Mamba-2’s perplexity at $\rightarrow$4, implying that a more expressive recurrence can reduce the required hidden-state bottleneck without loss (Lahoti et al., 16 Mar 2026). A plausible implication is that future Bottleneck Mamba Modules need not rely only on channel compression; they can also improve the quality of a small state.

5. Efficiency-oriented reinterpretations

Some work addresses bottlenecks in implementation rather than architecture. eMamba explicitly states that it does not introduce a fundamentally new bottleneck variant of the Mamba block. Instead, it retains the same logical sequence—normalization, parallel projections, gating path, SSM layer, output projection, and residual—but replaces LayerNorm with RangeNorm, approximates SiLU and exponentials with piecewise-linear segments, replaces Softplus with ReLU, and uses approximation-aware NAS to choose small expansion factors and state dimensions (Kim et al., 14 Aug 2025). The paper states that compactness arises from choosing minimal expansion $\rightarrow$5 and state dimension $\rightarrow$6 via NAS rather than from a classical narrow–wide–narrow redesign.

PackMamba similarly treats bottlenecks as operator-level inefficiencies in variable-length training. It packs shorter sequences into fixed-length segments, modifies conv1d and selective scan to preserve packing–unpacking invariance, and resets the SSM state at sequence boundaries by zeroing $\rightarrow$7 whenever a new packed segment starts (Xu et al., 2024). On an NVIDIA A100 GPU, it reports $\rightarrow$8 speedup on the 1.4B model and $\rightarrow$9 on the 2.8B model relative to the baseline single-sequence processing scheme (Xu et al., 2024).

These results suggest that the practical meaning of a Bottleneck Mamba Module depends on the optimization target. In some papers the bottleneck is channel width or token count; in others it is the cost of normalization, exponentiation, padding waste, or quadratic attention.

6. Limitations, misconceptions, and open directions

A recurrent misconception is that any Mamba block placed at low resolution is automatically a Bottleneck Mamba Module. The surveyed papers do not support that equivalence. Deform-Mamba explicitly places a convolutional MVC module, not Mamba, in the bottleneck (Ji et al., 2024). MobileMamba likewise states that its MRFFI is better characterized as a lightweight multi-branch interaction block rather than a classical bottleneck block, even though its WTE-Mamba branch contains an internal expand-then-compress pattern (He et al., 2024). eMamba also states that it does not redesign Mamba into a classical narrow–wide–narrow bottleneck (Kim et al., 14 Aug 2025).

A second misconception is that compression alone resolves Mamba’s failure modes. You et al. identify a different bottleneck: the locality of the $X'\in\mathbb{R}^{B\times C\times H\times W}$0 gating mechanism. Their Global Selection module augments

$X'\in\mathbb{R}^{B\times C\times H\times W}$1

and with only $X'\in\mathbb{R}^{B\times C\times H\times W}$2M extra parameters enables a 130M Mamba model to improve from $X'\in\mathbb{R}^{B\times C\times H\times W}$3 to $X'\in\mathbb{R}^{B\times C\times H\times W}$4 points on a distributed-information synthetic task (You et al., 2024). Mamba Modulation identifies yet another bottleneck, namely the spectrum of the transition matrix $X'\in\mathbb{R}^{B\times C\times H\times W}$5, and proposes layer-wise scaling of $X'\in\mathbb{R}^{B\times C\times H\times W}$6 during inference to improve long-context generalization (Lu et al., 23 Sep 2025). These papers indicate that bottlenecks may be representational or dynamical rather than merely architectural.

Several open directions are stated directly. G-Mamba notes that a true bottleneck adaptation with $X'\in\mathbb{R}^{B\times C\times H\times W}$7 bottlenecking would further reduce compute and parameters (Chan et al., 7 Jun 2026). InceptionMamba suggests further exploration of efficient multi-scale SSM hybrids (Wang et al., 10 Jun 2025). MambAdapter reports that streaming on Whisper sees approximately $X'\in\mathbb{R}^{B\times C\times H\times W}$8 higher per-step latency than batched or offline use, so very short or streaming inputs remain a practical constraint (Ali et al., 14 Jun 2026). MTR notes that static per-layer $X'\in\mathbb{R}^{B\times C\times H\times W}$9 may not be optimal for every block (Ma et al., 18 Jul 2025). Taken together, these results support a broad but technically precise conclusion: the Bottleneck Mamba Module is not a single standardized layer, but a family of designs that place Mamba’s selective state-space computation inside a deliberately constrained subspace, neck, or compression stage in order to trade off long-range modeling, parameter efficiency, and hardware efficiency.