Modality-Based Mixture-of-Experts (MoE)

Updated 18 August 2025

Modality-based MoE is a framework that divides complex tasks into modality-specific subtasks using dedicated expert subnetworks, enhancing specialization and efficiency.
It employs dynamic, attention-informed gating mechanisms to route inputs to the optimal expert, ensuring robust handling of missing or interleaved modalities.
Empirical results demonstrate improvements in accuracy and efficiency across applications like clinical decision support and scene understanding, confirming its practical value.

A modality-based mixture-of-experts (MoE) system divides the global learning task into sub-tasks aligned with structure or semantics specific to each data modality, engaging separate “expert” subnetworks whose parameters and routing are directly informed by the nature and combination of modalities present. In recent research, this paradigm has emerged as a fundamental building block for large-scale, efficient, and adaptable AI systems, particularly in environments with heterogeneous, missing, or interleaved modalities.

1. Architectural Principles of Modality-Based MoE

Modality-based MoE architectures are structured to exploit inherent modality distinctions and to enable conditional computation:

Expert Partitioning: Experts may be organized as modality-specific groups (e.g., separate text and image experts (Lin et al., 31 Jul 2024), intra-/inter-modality blocks (Wang et al., 13 Aug 2025), or per-modality knowledge experts (Zhang et al., 27 May 2024)). The construction enables each group to learn representations optimal for its input domain, reducing interference and enhancing specialization efficiency.
Routing Mechanisms: Most implementations utilize a gating function or router that determines which experts should process each token or sample. Routing is guided by modality tags, token type, representation similarity, intrinsic token content, or even cross-expert feedback. For example, “hierarchical” or “dynamic token-aware” routing first selects the modality group and then the expert within it (Lin et al., 31 Jul 2024, Jing et al., 28 May 2025).
Fusion Layer Integration: In unified models (such as Uni-MoE (Li et al., 18 May 2024), MoMa (Lin et al., 31 Jul 2024), or Flex-MoE (Yun et al., 10 Oct 2024)), modality-specific encodings are merged into a shared representation via connectors or adapters before processing by MoE-augmented transformer blocks. This layered modularity provides the backbone for cross-modal reasoning.
Missing Data Handling: Mechanisms like learnable missing modality banks (Yun et al., 10 Oct 2024) or indicator embeddings (Han et al., 5 Feb 2024) are used to signal or impute missing modalities, conditioning expert selection and maintaining robustness in incomplete or arbitrary modality combinations.
Expert Diversity and Disentanglement: Some architectures employ additional mechanisms—e.g., mutual information minimization (Zhang et al., 27 May 2024), data-driven regularization (Krishnamurthy et al., 2023), or contrastive objectives (Feng et al., 23 May 2025)—to ensure that each expert’s output is both specialized and complementary.

2. Routing and Specialization Strategies

The gating function lies at the core of modality-based MoE. Key approaches include:

Attentive Gating: Routing decisions attend to both input and expert outputs, as in attentive gating (Krishnamurthy et al., 2023), where the attention mechanism leverages expert key/query interactions to ensure sample-expert assignment reflects both input semantics and expert specialization.
Laplace/Distance-Based Gating: FuseMoE (Han et al., 5 Feb 2024) and others introduce gating functions based on L₂ or L₁ norms, such as Laplace gating, for improved sparsity and convergence properties. The gate computes expert affinity via $\text{Top-K}(-\lVert W - x \rVert_2)$ , yielding softened, stable expert selection.
Soft Modality-Aware Routing: SMAR (Xia et al., 6 Jun 2025) regularizes the difference between expert routing distributions for each modality using a symmetric KL divergence, allowing “soft” specialization without rigid architectural separation and preserving original model capabilities even as new modalities are added.
Hypernetwork-Aided Routing: Approaches such as HyperMoE (Zhao et al., 20 Feb 2024) and EvoMoE (Jing et al., 28 May 2025) use hypernetworks to dynamically generate router parameters, allowing token-by-token and modality-aware flexibility in assigning experts.
Dual/Hierarchical Routing: Flex-MoE (Yun et al., 10 Oct 2024) combines global and per-combination routing stages, first learning generalized knowledge from fully observed samples, then specializing experts/routing to sparse or missing-modality settings.

3. Theoretical and Empirical Perspectives on Expert Specialization

A central challenge in MoE training is to assure that experts do not collapse into redundant functions, which leads to underutilization and loss of modular capacity:

Entropy Metrics and Mutual Information: Quantitative analysis typically utilizes gating entropy ( $H_s$ for per-sample sparsity, $H_u$ for expert utilization) and the mutual information between expert assignment and task label, $I(E;Y)$ , to assess specialization quality and alignment with task decomposition (Krishnamurthy et al., 2023).
Regularization and Contrastive Term: Regularizers such as sample similarity-informed terms $L_s$ (Krishnamurthy et al., 2023), or contrastive InfoNCE objectives (Feng et al., 23 May 2025), maximize the information gap between activated and inactivated experts, pulling together the representations of relevant experts and pushing away the others, thus enhancing modularity.
Mutual Distillation: Moderate distillation among experts (Xie et al., 31 Jan 2024) is shown to alleviate the “narrow vision” problem, promoting both generalization and improved “expert probing” performance while avoiding homogenization that would occur at excessive distillation strengths.
Expert Evolution: Diverse expert initialization (as in EvoMoE (Jing et al., 28 May 2025), where new experts are iteratively “evolved” from a single trainable backbone via varying historical/gradient blending) circumvents the uniformity common in naive parameter copying.

Empirical evaluations across vision, NLP, clinical, and multi-modal benchmarks consistently report gains in accuracy, efficiency (FLOPs reduction), improved robustness to missing modalities, and more interpretable or balanced expert usage relative to dense or task-blind MoE baselines (Han et al., 5 Feb 2024, Zhang et al., 27 May 2024, Wang et al., 13 Aug 2025).

Robust real-world AI faces missing, incomplete, or variable combinations of modalities. Several modality-based MoE systems directly address this:

Missing Modality Bank: Flex-MoE (Yun et al., 10 Oct 2024) replaces missing modals with embedding vectors conditioned on the observed subset, rather than using generic padding or mean imputation, thus preserving context and facilitating clean expert specialization.
Specialized Router Design: The $\mathcal{G}$ -Router is used with fully observed data to endow general expertise to all experts; the $\mathcal{S}$ -Router then forces each sample with a given subset of modalities to activate a unique, dedicated expert, driving specialization by cross-entropy loss targeting the correct modality-combination expert.
Entropy and Entropic Regularization: Entropy-based regularization in routing (e.g., in FuseMoE (Han et al., 5 Feb 2024)) ensures that routers learn not only to ignore uninformative missing-indicator vectors but also to balance load across available experts.

This dual approach—explicit imputation for missing data and controlled routing for varying modality sets—underpins state-of-the-art robustness in domains such as clinical prediction with ADNI or MIMIC-IV data (Yun et al., 10 Oct 2024).

5. Integration in Unified and Multimodal Large Models

Scaling MoE to unified multimodal systems requires architecture and training innovations:

Connector Encoders and Soft Tokens: Frameworks such as Uni-MoE (Li et al., 18 May 2024), Uni3D-MoE (Zhang et al., 27 May 2025), and MoIIE (Wang et al., 13 Aug 2025) include modality-specific encoders and projection connectors to unify multiple streams (text, image, video, speech, 3D modalities) into a single representation space before MoE-layer processing.
Sparse MoE Layers: These models selectively replace dense FFN sublayers with MoE blocks, allowing only top- $k$ experts per token or token group to be activated, thus achieving both generalist capacity and modular specialization without incurring prohibitive computational load.
Progressive and Two-Stage Training: Approaches such as Uni-MoE's three-stage process (alignment, modality-specific expert training, and LoRA-based tuning) or MoIIE's two-stage regime (alignment followed by full MoE activation) systematically bootstrap generalized multimodal representation, followed by targeted expert specialization and fusion, facilitating both generalization and efficiency.
Joint Intra-/Inter-Modality Routing: MoIIE (Wang et al., 13 Aug 2025) allows tokens to pick both intra-modality and inter-modality experts, realizing joint processing of cross-modal associations (such as textual-visual alignment in vision-language inference) within a unified token routing framework.

6. Applications, Empirical Outcomes, and Limitations

Modality-based MoE frameworks have demonstrated empirical advantages in:

Multimodal Scene Understanding (Uni3D-MoE, MoIIE): Adaptive 3D or vision-language fusion for tasks like question answering, retrieval, and captioning, where token-level routing enables cross-modal context utilization.
Clinical Decision Support (FuseMoE, Flex-MoE, MedMoE): Handling missing or irregularly sampled modalities such as labs, imaging, and clinical text in practical healthcare scenarios.
Unified Multimodal Assistance (Uni-MoE): Efficient, bias-reducing instruction-following and reasoning across language, image, video, and audio/speech modalities.
Text-to-Speech Synthesis (MoE-TTS): Augmenting frozen LLMs with learnable speech-modality experts yields improved generalization to out-of-domain, stylistically complex descriptions (Xue et al., 15 Aug 2025).
Medical Vision-Language Alignment (MedMoE): Routing multi-scale image representations through context-selected experts to address variable spatial detail requirements and optimize alignment with clinical text (Chopra et al., 10 Jun 2025).

Despite these advances, challenges remain in avoiding expert collapse, balancing specialization vs. redundancy, efficiency, and ensuring graceful scaling to ever-larger and more diverse modality sets. In some cases, over-regularization (e.g., excessive distillation strength (Xie et al., 31 Jan 2024)) or depth sparsity (as in mixture-of-depths (Lin et al., 31 Jul 2024)) can degrade inference performance or specialization.

7. Theoretical Insights on Expressivity and Efficiency

Theoretical analysis has characterized the expressive power of MoEs for structured multimodal tasks (Wang et al., 30 May 2025):

Shallow MoEs: Capable of efficiently approximating functions defined on low-dimensional manifolds, thus readily modeling the intrinsic structure of each modality independently.
Deep MoEs: With $L$ layers and $E$ experts per layer, deep MoEs can model up to $E^L$ structured tasks or function pieces, leveraging compositional sparsity inherent in multimodal decompositions.
Architectural Roles: The gating mechanism assigns local regions (or modal subspaces) to experts; the specific architecture (number of layers and experts) determines granularity vs. compositional depth of the representable function class.

This theoretical grounding underpins the architectural choices and performance of practical modality-based MoE systems and suggests clear guidelines for matching depth, expert count, and gating complexity to real-world multimodal data distributions.

In summary, modality-based mixture-of-experts advances modular, efficient, and robust learning by aligning model specialization directly with the properties of multi-domain data. Through innovations in routing, regularization, and training strategies, and empirical validation across diverse domains, this approach establishes itself as a foundational principle in contemporary multimodal AI system design.