Bidirectional Mamba Model

Updated 12 July 2025

Bidirectional Mamba is a neural network architecture built on state space models that processes data in both directions to capture comprehensive contextual dependencies.
It employs fusion strategies such as averaging, concatenation, and learned gating to combine forward and backward outputs, overcoming unidirectional limitations.
Demonstrated across vision, speech, and biomedical signal processing, the model improves efficiency and accuracy while scaling linearly with sequence length.

A bidirectional Mamba model denotes a neural network architecture built upon state space models (SSMs) and designed to process input sequences in both forward and backward directions. By leveraging bidirectional scanning, these models overcome the primary axial limitation of standard Mamba—its unidirectional, causal recurrence—thereby yielding richer contextual representation, enhanced capacity for long-range dependencies, and increased modeling versatility across modalities. The bidirectional Mamba framework has been implemented and validated in a variety of domains including vision, time series analysis, speech, biomedical signal processing, point cloud analysis, scientific modeling, and multimodal tasks.

1. Foundations and Bidirectional State Space Modeling

A standard Mamba block employs a state space model where the hidden state evolution and output at each step are given (in continuous-domain) by: $h'(t) = Ah(t) + Bx(t) \qquad y(t) = Ch(t) + Dx(t)$ with discretization and hardware-aware parallel scan yielding linear time complexity in sequence length. In the unidirectional form, only historical information is encoded; to address this, bidirectional Mamba processes the entire sequence both forwards (left-to-right) and backwards (right-to-left), fusing both contexts.

Typical bidirectional fusion strategies include:

Simple addition/averaging: Process $x$ forward to produce $y_\text{fwd}$ , then reverse $x$ and process backward to yield $y_\text{bwd}$ ; combine outputs as $y_\text{bi} = \frac{1}{2}(y_\text{fwd} + y_\text{bwd})$ .
Concatenation: Particularly in image and scientific domains, outputs from forward and backward scans are concatenated for downstream processing.
Learned gating mechanisms: A learnable gate (e.g., a sigmoid-weighted MLP) adaptively merges forward and backward outputs, as in single-cell data modeling (2504.16956).

2. Technical Innovations, Variants, and Implementation

Multiple architectural patterns for bidirectional Mamba have emerged:

Dual-column and dual-path designs: Dual-path and dual-column Mamba (e.g., for speech separation and spoofing detection) process intra-segment and inter-segment information in both directions, increasing modeling of local and global features (2403.18257, 2411.10027).

Integrated local backward scan: LBMamba embeds a lightweight local backward scan within the main forward scan, avoiding a full extra pass and thus nearly halving computational and bandwidth costs versus global bidirectional scans (2506.15976).

# Pseudocode (LBMamba core mechanism)
for each local segment M in sequence:
    h_f = run_forward_scan(x in segment)
    h_b = run_backward_scan(x in segment)  # register-only, cheap
    h_comb = h_f + (h_b - Bf * x)  # fuse for bidirectional state

Bidirectional Mamba in hybrid models: Architectures such as PointABM and MaskMamba combine bidirectional Mamba with Transformer attention layers, assigning SSMs to value or global feature extraction branches, and merging output via residuals or concatenation (2406.06069, 2409.19937).
Adaptive bidirectionality for context-specific fusion: In sequential recommendation and time series, partial flips (PF-Mamba) and selective gate layers enable joint modeling of immediate past and selected future elements, optimizing short- and long-range dependency modeling (2408.11451, 2404.15772).

3. Application Domains and Empirical Results

Bidirectional Mamba variants have demonstrated strong empirical performance and efficiency across a spectrum of tasks:

Vision: Vision Mamba ("Vim") matches or surpasses transformer baselines such as DeiT for image classification, detection, and segmentation, at up to 2.8 $\times$ speedup and 86.8% less memory in high-resolution batch inference (2401.09417). Locally bidirectional LBVim models further improve the accuracy-throughput Pareto front with minimal computational overhead (2506.15976).
Speech and Audio: Dual-path, dual-column, and ablation-enhanced BiMamba models outperform or compete with state-of-the-art RNN, CNN, and transformer systems for speech separation and spoofing detection, while reducing parameter count and memory (2403.18257, 2405.12609, 2411.10027).
Time Series: S-Mamba and Bi-Mamba+ achieve leading accuracy for both short- and long-term prediction, with linear scaling, through bidirectional correlation encoding and memory-efficient, adaptive fusion blocks (2403.11144, 2404.15772). In probabilistic imputation, bidirectional backbone design in DiffImp leads to strong results under high missingness (2410.13338).
Biomedical Signals: Bidirectional Mamba is central in scalable, efficient EEG analytics for both classification and artifact detection (FEMBA, BiT-MamSleep), as well as multimodal sleep staging, with performance and scalability on par or superior to transformer-style approaches (e.g., 0.949 AUROC for artifact detection with 7.8M-parameter FEMBA-Tiny) (2502.06438, 2411.01589, 2405.20142).
Non-Euclidean and Scientific Data: For spherical manifolds (Surface Vision Mamba), bi-SSM blocks enable domain-agnostic modeling of large-scale cortical surface data, offering 4.8 $\times$ speed and 91.7% lower memory than attention-based baselines (2501.14679). For anomalous diffusion, Bi-Mamba achieves robust segmentation and estimation from short, noisy trajectories, outperforming bidirectional RNNs in mean absolute error and F1 error in the AnDi-2 challenge (2412.07299).
Single-cell Transcriptomics: GeneMamba leverages bidirectional state-space updates and pathway-aware contrastive pretraining to efficiently model gene-gene interactions and batch effects in 30+ million cell datasets, with strong annotation and batch integration results (2504.16956).
Medical Image Translation: ABS-Mamba employs a bidirectional spiral-scanning state-space mechanism (BMRN), harmonizing anatomical semantics and detail preservation in multi-modal medical image synthesis, outperforming established architectures on SSIM/PSNR (2505.07687).

4. Efficiency Considerations and Hardware-Alignment

Bidirectional Mamba models are structured for near-linear computational and memory overhead in sequence length or spatial patch count—crucial for high-resolution vision, long signal timeseries, and large biological datasets:

Parallel selective scan execution is a core design feature, enabling high-throughput implementations, especially with hardware-aligned local backward pass as in LBMamba (2506.15976).
Parameter sharing and convolution-based SSMs further minimize resource footprint in tasks such as sleep analytics and cortical mapping (2411.01589, 2501.14679).
Hybridization (SSM/attention) enables selective assignment of SSM and self-attention to different architectural roles for further efficiency/context trade-off tuning (2406.06069, 2409.19937).
In sensitivity studies, bidirectional mechanisms can provide interpretable attribution maps identifying features and regions most influential to downstream predictions, with clinical and biological applications (2501.14679, 2504.16956).

5. Limitations and Ongoing Advances

While bidirectional SSM modeling addresses core context limitations of unidirectional Mamba, further advances are being explored:

Hybrid and local-global scanning: LBMamba exemplifies the migration from global to local bidirectional scans, enabling faster, more memory-efficient execution while retaining accuracy. Layer-level alternation and hardware-resident scanning reduce bandwidth and inter-thread communication (2506.15976).
Fusion with attention and multimodal interaction: Cross-Mamba modules and Transformer hybrids allow bidirectional Mamba to leverage modality cross-talk and more nuanced context fusion, as required in vision-language and image generation workflows (2502.15130, 2409.19937).
Task-specific gating, adaptation, or skip mechanisms: Adaptive selection and series-relation deciders optimize Mamba’s bidirectional capacity across variable intra- and inter-series correlations, biological variability, and domain-specific structural requirements (2404.15772, 2504.16956).
Challenges in global summarization: For certain use cases relying on special tokens (e.g., class token summarization in vision), incorporating full bidirectional context remains non-trivial in local scanning designs (2506.15976). Improving dedicated token support is a known research direction.

6. Implications and Future Outlook

The bidirectional Mamba model has emerged as a scalable, efficient, and versatile architecture that can serve as a generic backbone for diverse data types and tasks. Its capacity to efficiently encode global context and long-range dependencies with linear complexity, combined with robust task performance, positions it as a viable alternative and complement to transformer-based models in both research and application settings. Ongoing innovations in bidirectionality, hybridization, hardware-alignment, and biologically informed objectives are likely to expand the range and depth of its practical utility.