Cross-modal Bidirectional Mamba2 Integration

• CBMI is a neural network architecture that employs the Mamba2 state space model for efficient, scalable fusion of heterogeneous data modalities like vision and language.
• It overcomes quadratic attention limitations by using bidirectional scanning and specialized fusion modules, such as SSCS and DSSF, to align disparate modality features.
• CBMI frameworks demonstrate improved performance in tasks like object detection and VQA, with gains such as a +5.9% mAP boost and up to 3× faster inference.

Cross-modal Bidirectional Mamba2 Integration (CBMI) encompasses a class of neural network architectures and mechanisms that leverage the Mamba2 State Space Model (SSM) for efficient, robust fusion of heterogeneous modalities (e.g., vision and language, RGB and infrared, audio and video) through bidirectional interaction and specialized scanning. This approach is designed to overcome limitations of quadratic-complexity attention, modality disparity, and context misalignment. CBMI enables precise, scalable multimodal integration for dense prediction, object detection, VQA, image synthesis, and domain-specific tasks such as surgical scene understanding.

1. Foundations and Motivations

CBMI is motivated by the need for scalable, high-fidelity fusion of heterogeneous data modalities in domains where conventional Transformer-based models (with quadratic context complexity) or static fusion approaches underperform. Key challenges addressed by CBMI frameworks include:

• Modality heterogeneity: Different sensor data often differ in resolution, spatial/temporal distribution, or semantic context, challenging naive feature concatenation or single-layer fusion methods (Dong et al., 14 Apr 2024, &&&1&&&).
• Computational scalability: Quadratic attention limits sequence length and the number of high-dimensional features that can be processed efficiently (Huang et al., 29 Jul 2024, Li et al., 31 Mar 2025).
• Bidirectional interaction: Rich cross-modal representations require information flow in both directions (e.g., vision-informed language and vice versa), best achieved through cascaded or concurrent attention/gating mechanisms (Dong et al., 11 Oct 2024, Li et al., 7 Jan 2025).

Mamba2-based models—characterized by linear scalability, selective state space scanning, and structured temporal/spatial modeling—provide the core technological advantage for modern CBMI approaches (Huang et al., 29 Jul 2024, Hao et al., 20 Sep 2025).

2. Core CBMI Mechanisms and Architectures

CBMI solutions center around cross-modal modules that systematically align, interact, and integrate information from each modality, most notably using bidirectional communication and scanning. Common architectural elements include:

a. State Space Channel Swapping (SSCS) and Dual State Space Fusion (DSSF)

In cross-modality object detection, the Fusion-Mamba Block (FMB) employs:

• SSCS: Shallow fusion by splitting and interchanging channels across modalities, followed by visual state-space (VSS) processing (Dong et al., 14 Apr 2024).
• DSSF: Deep fusion in a hidden state space via dual (bidirectional) gated attention, ensuring both branches (e.g., RGB and IR) receive and deliver complementary information.

b. Dual Alignment Modules

AlignMamba introduces:

• Local token-level alignment: Using Optimal Transport (OT) to create explicit correspondence matrices between modality tokens, minimizing distance (e.g., cosine) with relaxed or full constraints (Li et al., 1 Dec 2024).
• Global distribution-level alignment: Employing Maximum Mean Discrepancy (MMD) to minimize Hilbert-space mean differences among projected features, regularizing representations across modalities.

c. Scan-based Bidirectional Integration

Various CBMI models leverage advanced scanning strategies to impose causal or spatial order for SSM processing:

• Visual Selective Scanning: E.g., bidirectional/cross-scan (row, column, diagonal, spatial radial) transformations render 2D image patches sequentially processable by Mamba2 LLMs or surgical assistants (Huang et al., 29 Jul 2024, Hao et al., 20 Sep 2025).
• Surgical Instrument Perception (SIP) Scan: Radially traverses surgical scene images based on domain priors, aligning instrument trajectories with visual features (Hao et al., 20 Sep 2025).
• Bidirectional Interaction Scan (BI-Scan): Integrates task- and position-first serializations, feeding both directions into parallel SSMs for dense multitask prediction (Cao et al., 28 Aug 2025).

d. Multimodal Connectors and Bridges

• Mamba2 Scan Connector (MSC): Bridges vision encoders and LLMs via sophisticated token rearrangement and nonlinear transformation (e.g., SwiGLU-coupled pipelines) (Huang et al., 29 Jul 2024).
• Cross-Mamba Modules: Cross-attention analogues for SSM, aligning language and vision by matching time scales, enabling bidirectional fusion in a pure-Mamba context (Chen et al., 21 Feb 2025).

3. Mathematical Formulation and Information Flow

Key mathematical formulations underpin CBMI’s bidirectional alignment and fusion:

Module/Step	Mathematical Expression / Algorithmic Step
Local OT Alignment	$$C_{v2l}(i, j) = 1 - \frac{X_v^i \cdot X_l^j}{\|X_v^i\|_2 \|X_l^j\|_2}$$; $M_{v2l}(i, j)$ selects minimal-cost pairing
Global MMD Alignment	$$\text{MMD}^2(X, Y) = \frac{1}{T^2} \sum_{i,i'} k(x_i, x_{i'}) + \frac{1}{T^2} \sum_{j,j'} k(y_j, y_{j'}) - \frac{2}{T^2} \sum_{i,j} k(x_i, y_j)$$
Bidirectional Fusion in SSCS/DSSF	$$y'_R = y_R \cdot \text{SiLU}(z_R) + y_{IR} \cdot \text{SiLU}(z_R)$$; $$y'_{IR} = y_{IR} \cdot \text{SiLU}(z_{IR}) + y_R \cdot \text{SiLU}(z_{IR})$$
CBMI Output Aggregation	$$S = S_{forward} \cdot \sigma(F_v) + S_{backward} \cdot \sigma(F_v)$$; $S_{output} = \text{Linear}(\text{LN}(S))$ (Hao et al., 20 Sep 2025)

Information flow is often realized through a sequence: modality-specific encoding → local and global alignment → serial/parallel state space modeling and bidirectional fusion → downstream prediction modules.

4. Benchmark Results and Performance Characteristics

CBMI-driven architectures consistently demonstrate superior or competitive results in multiple domains, attributed to their ability to both harmonize disparate input distributions and accelerate inference. Results include:

• Object detection (Fusion-Mamba): +5.9% mAP (M³FD), +4.9% mAP (FLIR-Aligned), outperforming YOLO-MS, ICAFusion, CFT, with linear complexity and reduced latency (Dong et al., 14 Apr 2024).
• Multimodal LLM (ML-Mamba): Matches/outperforms TinyLaVA/MobileVLM v2 in VQA-v2 and POPE with over 3× faster inference (171 vs. 50 tokens/s) (Huang et al., 29 Jul 2024).
• Dense medical image synthesis (ABS-Mamba): SSIM 0.896, PSNR 28.32 on SynthRAD2023-brain, improvements over I2I-Mamba and VMamba (Yuan et al., 12 May 2025).
• Surgical VQLA (Surgical-MambaLLM): Significant accuracy, F-score, mIoU improvement versus Surgical-VQLA++ and EnVR-LPKG, enabled by SIP scanning and CBMI fusion (Hao et al., 20 Sep 2025).
• Referring segmentation (CroBIM): State-of-the-art [email protected], mIoU, and oIoU metrics on RISBench (Dong et al., 11 Oct 2024).
• Multi-task dense prediction (BIM): mIoU 57.40 (NYUD-V2), outperforming MTMamba while maintaining linear computational scaling (Cao et al., 28 Aug 2025).

5. Comparison with Transformers and Other Approaches

CBMI implementations leveraging Mamba2 SSM architectures exhibit:

• Superior scalability: Linear scaling in both sequence length and number of modalities, critical for real-time and resource-limited environments (Huang et al., 29 Jul 2024, Li et al., 31 Mar 2025).
• Bidirectional information flow: Dual attention, scan, or distillation paths, surpassing static or one-way fusion common in linear or attention-only schemes (Dong et al., 14 Apr 2024, Dong et al., 11 Oct 2024).
• Robustness to missing modalities: Explicit local/global alignment and token-wise matching provide resilience in cases of incomplete input (Li et al., 1 Dec 2024).
• Cross-architecture adaptability: Cross-architecture distillation (TransMamba) enables knowledge transfer from large Transformer models to lighter, efficient Mamba2-based models while retaining accuracy (Chen et al., 21 Feb 2025, Li et al., 31 Mar 2025).
• Application-specific scanning: CBMI adapts its sequence conversion (SIP, MS-Scan, BI-Scan) to domain constraints, e.g., spatial coherence in medical imaging or instrument-aware scan paths in surgery (Yuan et al., 12 May 2025, Cao et al., 28 Aug 2025, Hao et al., 20 Sep 2025).

6. Applications and Domain-Specific Extensions

CBMI frameworks have been validated or proposed in a wide range of scenarios, including but not limited to:

• Autonomous driving and surveillance: Cross-modal detection in low-light or adverse conditions (RGB + thermal/IR/image fusion) (Dong et al., 14 Apr 2024).
• Robotic and clinical surgery: Enhanced spatial understanding for instrument localization, visual reasoning, question-answering, domain-specific scan paths (Hao et al., 20 Sep 2025).
• Dense video and remote sensing: Pixel-precise referring segmentation in data with complex spatial/semantic cues, using bidirectional, cascaded attention (Dong et al., 11 Oct 2024).
• Medical image translation: Organ-aware, high-resolution cross-modality synthesis (CT ↔ MRI) with spiral, bidirectional SSM (Yuan et al., 12 May 2025).
• High-throughput commercial recommendation: Scalable recommendation with DREAM local attention and MMD global regularization (Ren et al., 11 Sep 2025).
• Knowledge transfer and universal adaptation: Efficient cross-architecture training for resource-constrained or new domains through bidirectional distillation and subcloning (Chen et al., 21 Feb 2025, Li et al., 31 Mar 2025).

7. Limitations, Implications, and Prospects

While CBMI offers robust multimodal integration and inference efficiency, several observations and avenues for further research are noted:

• Alignment saturation: Benefits from stacking additional alignment/fusion modules begin to diminish after a threshold, calling for adaptive or dynamic mechanisms (Dong et al., 14 Apr 2024).
• Generalization to more modalities: Current frameworks focus on two or three streams; extending dual or cyclic bidirectional matching to $N$ modalities remains a challenge (Li et al., 1 Dec 2024).
• Task-adaptive scanning: Frameworks such as SIP or MS-Scan demonstrate that domain-aware sequence conversion can boost performance, suggesting further room for custom scan strategies (Hao et al., 20 Sep 2025, Cao et al., 28 Aug 2025).
• Compositionality and grounding: For VQA, localization, and surgical interpretation, grounding visual tokens in complex text queries remains an open research area (Hao et al., 20 Sep 2025).
• Scalability in low-resource or federated settings: The efficient transfer and adaptation mechanisms, such as LoRA+ and adaptive bidirectional distillation (WSAB), suggest applicability for large-scale, privacy-sensitive, or distributed deployment (Yuan et al., 12 May 2025, Chen et al., 21 Feb 2025).

A plausible implication is that continued advances in CBMI will further unify the strengths of attention and state space mechanisms, enable real-time, robust multimodal reasoning, and facilitate deployment in increasingly complex, domain-specific applications.