Modality-driven Mixture-of-Experts

• Modality-driven Mixture-of-Experts is a neural architecture that assigns specialized subnetworks based on input modality to enhance capacity and precision.
• Advanced routing mechanisms, including modality-aware, interaction-aware, and task-conditioned gating, dynamically tailor inference paths for diverse multimodal data.
• Innovative training protocols such as expert evolution, load-balancing, and regularization yield efficient, interpretable, and robust multimodal fusion across applications.

Modality-driven Mixture-of-Experts (MoE) refers to a class of neural architectures in which expert subnetworks are assigned, selected, or specialized based on the modality (e.g., text, image, audio, point cloud) of the input or its fine-grained characteristics. These models are designed to maximize both capacity and specialization for diverse and heterogeneous multimodal data. The core idea is to engineer both the expert parameterization and the gating/routing mechanisms such that representations and inference paths adapt explicitly to the information structure and requirements of each modality, overcoming expert uniformity and router rigidity endemic to earlier approaches.

1. Architectures and Routing: Modality-aware Expert Specialization

Modality-driven MoE systems instantiate specialized subnetworks (experts) targeting distinct modalities, sub-modalities, or cross-modal interactions. Architectural strategies vary:

• Partitioned Pools: Experts are partitioned along modality boundaries, e.g., intra-text, intra-vision, and shared inter-modality as in MoIIE (Wang et al., 13 Aug 2025), or text/image groupings as in MoMa (Lin et al., 31 Jul 2024).
• Modality-Conditioned Routing: Dynamic routers dispatch tokens to experts according to their modality or encoded modality-preference, typically exploiting token metadata or learned affinity, e.g., separate hypernetworks for text/vision routing in EvoMoE (Jing et al., 28 May 2025), modality-aware bias in SMAR (Xia et al., 6 Jun 2025), or hard modality-indexed paths in MoE-TTS (Xue et al., 15 Aug 2025).
• Interaction-aware Routing: Advanced schemes model not just outright modality but the structure of inter-modal relationships or temporal dependency, as exemplified by Time-MoE’s redundancy-uniqueness-synergy (RUS) informed router (Han et al., 30 Sep 2025) or I2MoE’s decomposition into uniqueness, redundancy, and synergy experts (Xin et al., 25 May 2025).
• Task- or Query-conditioned Gating: Specialized contexts, such as medical imaging report types (MedMoE (Chopra et al., 10 Jun 2025)) or 3D query semantics (Uni3D-MoE (Zhang et al., 27 May 2025)), inform the router so experts are activated adaptively across more granular axes than raw modality.

The table below summarizes a selection of reported modality-driven MoE expert partitionings:

Model	Expert Partitioning	Router Type
EvoMoE	N evolved FFNs; per-modality subrouters	Token/hypernet, dynamic, DTR
AsyMoE	ℰ_V (visual), ℰ_S (hyperbolic), ℰ_L-evd	Modality & evidence-aware
MoIIE	Intra-text, intra-vision, shared	Modality-gated softmax
MoMa	Text-experts, image-experts	Modality-indexed, expert-ch.
SMAR	Shared pool, modality-aware bias	Softmax+KL reg.
MoE-Health	Experts per observed modality subset	Sample-MLP, pattern-aware

2. Modality-driven Expert Initialization and Evolution

Standard MoE architectures typically initialize experts by duplicating a core FFN, often resulting in homogeneous function approximators. EvoMoE introduces a progressive "expert evolution" approach where one core FFN is directly learned and the other N-1 experts are periodically synthesized by mixing its weights with its most recent gradient, using a stochastically sampled “evolution rate” β to inject drift and diversification (Jing et al., 28 May 2025):

$$\theta_n \leftarrow \beta \theta_1 + (1-\beta)\nabla\theta_1$$

Stagewise freezing ensures only the main expert is tuned after initialization, countering expert uniformity and promoting specialization exploitable by a content- and modality-sensitive router.

Plausibly, such strategies—contrasting with static replication or uniform LoRA injection—modulate the diversity versus parameter-sharing trade-off, facilitating learned expert heterogeneity specifically harnessed by adaptive modality-aware routing.

3. Routing Mechanisms Encoding Modality

Advances in gating distinguish sophisticated modality-driven MoE from generic sparse MoE:

• Separate Hypernetwork Routers: EvoMoE deploys distinct routing hypernets for text vs. visual tokens. Each router produces token- and modality-conditioned gating weights:

$$\mathcal{E}^\tau = \Theta^{\tau}_{up}\bigl(\mathrm{SwiGLU}(\Theta^{\tau}_{down} z')\bigr),\quad \rho^\tau = \text{softmax}(\phi(\mathcal{E}^\tau))$$

where τ indexes modality, and routing weights are dynamically generated per token (Jing et al., 28 May 2025).

• Modality-aware Bias and KL Regularization: SMAR adds distinct bias vectors for each modality to the router’s output logits and uses symmetric KL divergence to softly encourage routing distributions to diverge (hence specialize) but remain balanced between modalities (Xia et al., 6 Jun 2025).
• Routing via Gating Network and Hard Modality-based Paths: MoE-Health routes based on the observed combination of available modalities, using a pattern-encoded embedding and a top-k gating MLP. Missing modalities are encoded with special token embeddings, so the router naturally omits incompatible experts (Wang et al., 29 Aug 2025).
• Temporal and Interaction-based Routing: Time-MoE computes per-token, per-modality lagged RUS statistics (redundancy-uniqueness-synergy) to drive context-dependent routing, ensuring experts acquire not only modal but interactional specialization (Han et al., 30 Sep 2025).

These mechanisms yield both sharp specialization (experts attend to appropriate tokens) and cross-modal adaptivity (shared experts for interaction learning).

4. Training Protocols and Objective Regularization

Modality-driven MoE architectures are typically trained with auxiliary mechanisms that maintain efficiency and avoid collapse of routing/expert specialization:

• Stagewise Training: Most systems employ a first stage aligning single-modality or connector modules (e.g., visual tokens to language space) with the backbone frozen, then a second stage where all experts and the router are optimized jointly on mixed-modality data (MoIIE (Wang et al., 13 Aug 2025), Uni-MoE (Li et al., 18 May 2024), EvoMoE (Jing et al., 28 May 2025)).
• Expert Load-Balancing: Load-balancing losses (as in Fedus et al. 2022) penalize highly skewed router distributions.
• Modality Regularization: In SMAR, a KL-divergence-based regularizer ensures inter-modality separation in routing probabilities while preventing excessive gating collapse; in AsyMoE, cross-modal partial-order/hyperbolic losses encourage hierarchical interaction modeling (Zhang et al., 16 Sep 2025).
• Weakly-Supervised Interaction Losses: I2MoE applies masked-modality perturbations to force each expert to specialize in unique information, redundancy, or synergy, backed by triplet margin and cosine similarity auxiliary losses (Xin et al., 25 May 2025).
• Curriculum Scheduling: Warm-up strategies (e.g., evidence-priority α scheduling in AsyMoE) prevent early over-specialization or degraded multimodal coverage (Zhang et al., 16 Sep 2025).

These protocols empirically yield more robust multimodal fusion, expert diversity, and improved resistance to data imbalance or catastrophic forgetting of unimodal capability.

5. Empirical Results and Efficiency Considerations

Across tasks and modalities, modality-driven MoE frameworks consistently deliver gains in performance, efficiency, and robustness compared to dense or unimodal baselines:

• Accuracy Improvements: EvoMoE surpasses MoE-LLaVA baselines by +1–2.8 points on multimodal benchmarks (MMBench, TextVQA, POPE) with fewer active experts per token (Jing et al., 28 May 2025). AsyMoE reports +26.58% and +15.45% over vanilla and modality-specific MoE, respectively (Zhang et al., 16 Sep 2025). MoIIE outperforms both dense and previous MoE-based LVLMs at lower activated parameter count (Wang et al., 13 Aug 2025).
• Parameter Efficiency: Modality-aware partitioning (MoMa) yields up to 5.2× FLOP reduction for image processing in early-fusion LLMs versus a compute-matched dense baseline (Lin et al., 31 Jul 2024). AsyMoE achieves a 42% reduction in activated parameters versus dense models without loss of accuracy (Zhang et al., 16 Sep 2025).
• Robustness with Incomplete Modalities: MoE-Health shows sustained gains across all combinations of missing/available modality patterns, confirming flexible deployment in real-world clinical settings (Wang et al., 29 Aug 2025).
• Language Preservation: SMAR achieves 86.6% retention of core language metrics using only 2.5% pure-text data—superior to prior MoE and dense methods—while still gaining multimodal performance (Xia et al., 6 Jun 2025).
• Interpretability: I2MoE and Time-MoE provide both global and local post-hoc interpretability for gating—showing which experts are responsible for sample-level predictions and reflecting meaningful decompositions of interaction modes out-of-the-box (Xin et al., 25 May 2025, Han et al., 30 Sep 2025).

6. Notable Applications and Modal Scope

Modality-driven MoE architectures have been systematically explored across a wide breadth of settings:

• General Vision-LLMs: LVLMs for image, text, video, audio, and speech mixtures (EvoMoE (Jing et al., 28 May 2025), Uni-MoE (Li et al., 18 May 2024), MoIIE (Wang et al., 13 Aug 2025), MoMa (Lin et al., 31 Jul 2024)).
• 3D Scene and Spatial Understanding: Token-level expert specialization for multi-view RGB, depth, BEV, point cloud, and voxel representations in Uni3D-MoE (Zhang et al., 27 May 2025).
• Biomedical and Clinical Data: Separate expert streams for EHR, clinical notes, and medical images (MoE-Health (Wang et al., 29 Aug 2025)), modality-specialized fusion and attention for X-ray, CT, MRI, ultrasound (MedMoE (Chopra et al., 10 Jun 2025)).
• Text-to-Speech (TTS): Distinct text and speech expert paths with hard routing to adapt frozen LLMs to high-fidelity, out-of-domain TTS via MoE-TTS (Xue et al., 15 Aug 2025).
• Interpretable and Interaction-aware Fusion: Explicit decomposition of fusion roles (uniqueness, redundancy, synergy) with end-to-end reweighting for transparent, explainable predictions (I2MoE (Xin et al., 25 May 2025), Time-MoE (Han et al., 30 Sep 2025)).

Many architectures natively extend to unseen or additional modalities (e.g., audio, multi-lingual, genomics), and are designed to facilitate "graceful degradation" or robust prediction when only a subset of modalities are available.

7. Future Directions and Limitations

Several avenues for further paper and refinement are noted:

• Dynamic or Learnable Expert Pool Partitioning: Current systems mostly fix the modality-to-expert structure; dynamic adaptation or meta-learning expert pools remains an open direction (Zhang et al., 16 Sep 2025).
• Geometric Fusion Models: Hyperbolic, Poincaré, or projective domains for cross-modal interaction (as in AsyMoE) provide greater representational flexibility at the price of additional computational cost.
• Locality and Region-aware Routing: Integrating spatially aware concept of routing—e.g., for image patches, or point cloud segments—to specialize experts at sub-modality resolution (Zhang et al., 16 Sep 2025, Zhang et al., 27 May 2025).
• Interpretability, Auditing, and Trust: Enhanced explanation of routing decisions to support deployment in high-stakes settings (e.g., clinic) (Xin et al., 25 May 2025, Wang et al., 29 Aug 2025).
• Optimal Regularization: Balancing routing collapse versus over-specialization via KL, entropy, or custom regularizers remains empirically sensitive (see SMAR ablation (Xia et al., 6 Jun 2025)).
• Scalability and Multi-lingual, Multi-modal Extension: Hard-gated, frozen-backbone MoE such as MoE-TTS provide a blueprint for efficient adaptation in future multi-domain generative models (Xue et al., 15 Aug 2025).

A plausible implication is that the integration of information-theoretic, geometric, and content-aware routing with scalable expert parameterization will continue to be central for the next generation of multimodal, foundation, and domain-specialist models. Modality-driven MoE is now a cornerstone methodology for efficient, robust, and interpretable multimodal intelligence.