Hierarchical MoE Feature Fusion Module

• Hierarchical MoE feature fusion module is a neural architecture that uses cascading expert networks with dynamic gating to aggregate intra-level and inter-level representations.
• It adaptively routes features by employing softmax and Top-K gating mechanisms, enabling specialization and efficient computation across multi-modal and multi-scale inputs.
• Empirical evaluations demonstrate significant accuracy improvements and computational savings in domains like audio deepfake detection, medical imaging, and autonomous driving.

A Hierarchical Mixture-of-Experts (MoE) Feature Fusion Module is a class of neural architecture for multi-level adaptive feature aggregation that leverages a stack or cascade of expert networks, each equipped with a dynamic gating mechanism, to orchestrate fine-grained information flow across both within-level (intra-level) and across-level (inter-level) representations. This paradigm is designed to maximize model expressivity in heterogeneous, multi-modal, or multi-scale domains by exploiting specialization, dynamic routing, and context-aware fusion at multiple abstraction layers.

1. Key Principles and Motivations

The fundamental principle underlying hierarchical MoE fusion is to address the limitations of static or flat (single-layer) aggregation schemes in dealing with redundant, noisy, or contextually variable information across levels or modalities. Hierarchical MoE structures are explicitly motivated by:

• Multi-granularity requirements: Many domains, such as speech (Hao et al., 4 Sep 2025), vision (Cai et al., 16 Nov 2025), medical imaging (Płotka et al., 8 Jul 2025), and multi-modal text-image problems (Liu et al., 21 Jan 2025), inherently encode essential cues at different granularities (e.g., shallow phonetic vs. deep semantic features).
• Dynamic selection and adaptivity: Redundant or irrelevant information varies across data instances and scales. Adaptive routing via expert selection allows the model to focus capacity where needed, improving robustness and generalization.
• Separation of concerns: Hierarchical structures allow lower-level MoEs to perform specialized, local refinement while higher-level MoEs can modulate or fuse the representations globally or in a task-specific fashion.

2. Canonical Architectural Patterns

Hierarchical MoE fusion modules are instantiated in a variety of architectural forms. Typical topologies include:

• Two-stage (cascaded) MoE: An initial layer (intra-level) of experts processes or refines feature sets within a modality, stage, or group. Their outputs are subsequently fused by a higher-order (inter-level) MoE that operates across modalities/layers or on globally aggregated features (Hao et al., 4 Sep 2025, Cai et al., 16 Nov 2025, Zhang et al., 24 Jan 2025, Wang et al., 14 Dec 2024, Płotka et al., 8 Jul 2025).
• Multi-branch/forked expert networks: Parallel expert "heads" are associated with separate modalities or layer depths (e.g., three experts for different CNN blocks (Cai et al., 16 Nov 2025), modular backbones (Xiang et al., 11 Aug 2025)).
• Gated fusion paths: Explicit gating or sparse routing mechanisms are implemented via lightweight networks (linear, MLP, attention, or self-attention routers). Gating can be soft (probabilistic, with top-K selection (Hao et al., 4 Sep 2025, Płotka et al., 8 Jul 2025)) or hard (top-1 gating with only one active expert, typically during inference (Xiang et al., 11 Aug 2025)).

Table: Example Hierarchical MoE Patterns

Reference	Lower-Level Experts	Higher-Level Fusion
(Hao et al., 4 Sep 2025)	Per-layer features (weighted)	Top-K sparse expert MoE
(Cai et al., 16 Nov 2025)	Parallel deep CNN branches	MoE-based contrastive/classifier
(Płotka et al., 8 Jul 2025)	Grouped token local MoE (SMoE)	Global token MoE (SMoE)
(Wang et al., 14 Dec 2024)	Hierarchical decoupling of features	Attention-triggered MoE
(Liu et al., 21 Jan 2025)	Per-modal/token MoEs	Interaction-gated fusion MoE
(Zhang et al., 24 Jan 2025)	Modality-specific cross-modal MoEs	Temporal-aware MoE

3. Gating and Routing Mechanisms

Hierarchical MoE modules rely on sophisticated gating and routing strategies to perform dynamic selection of both expert pathways and feature relevance at multiple levels:

• Softmax and Top-K gating for adaptivity and sparsity: A softmax over expert scores yields a distribution; Top-K selection imposes sparsity, as in the HA-MoE for audio (Hao et al., 4 Sep 2025) and HoME (Płotka et al., 8 Jul 2025).
• Hierarchical weighting vectors: Variable importance is learned for different abstraction levels, e.g., a learned per-layer importance vector $V_h$ that re-scales transformer outputs (Hao et al., 4 Sep 2025).
• Attention-triggered gating: Attention mechanisms (self-attention or cross-modal attention) derive per-instance weights for expert outputs, e.g., multi-head attention in ATMoE (Wang et al., 14 Dec 2024).
• Contextual and interaction-aware gating: Gating networks consume representations of predicted agreement, semantic alignment, or temporal signals to infer the optimal expert fusion scenario (e.g., in MIMoE-FND (Liu et al., 21 Jan 2025) and HM4SR (Zhang et al., 24 Jan 2025)).

4. Feature Fusion Strategies and Losses

Feature fusion in hierarchical MoE modules is characterized by:

• Hierarchical aggregation: Lower-level outputs are selectively combined at higher levels, often concatenated, pooled, or summed with learned weights.
• Residually-corrected fusion: Some designs employ residual connections and learned scalars to prevent over-correction or destabilizing feature drift (Zhang et al., 24 Jan 2025).
• Contrastive and classification objectives: Multi-level contrastive learning is used to enforce discriminativeness and compactness at each fusion stage (Cai et al., 16 Nov 2025). Cross-entropy and auxiliary losses (e.g., load-balancing, router-Z, balance loss) regularize expert utilization and promote specialization (Liu et al., 21 Jan 2025, Xiang et al., 11 Aug 2025).
• Explicit temporal or semantic conditioning: In sequential recommendation (Zhang et al., 24 Jan 2025), MoE layers directly incorporate explicit interval and timestamp information as gating inputs, enabling dynamic user interest modeling.

5. Domain-Specific Implementations and Empirical Insights

Hierarchical MoE fusion architectures have demonstrated significant performance improvements and increased robustness across domains:

• Audio deepfake detection: HA-MoE achieved 20–30% relative EER improvements, enabling both coarse-level and fine artifact detection by leveraging hierarchical weighting and gated expert fusion (Hao et al., 4 Sep 2025).
• Ultrasound plane recognition: SEMC’s two-stage SSFM+MCRM structure yielded stepwise gains in accuracy and F1 through shallow-deep fusion, multi-level contrastive learning, and MoE-based classification (Cai et al., 16 Nov 2025).
• 3D medical segmentation: HoME's local-to-global SMoE overcame quadratic complexity bottlenecks in long sequence modeling, establishing new accuracy records with linear compute scaling (Płotka et al., 8 Jul 2025).
• Multi-modal sequential recommendation: Hierarchical MoEs provided superior disentanglement of modality-specific signal and explicit temporal modulation, outperforming flat fusion schemes by 5–20% on ranking metrics (Zhang et al., 24 Jan 2025).
• Autonomous driving (BEV perception): CBDES MoE's functional modularity with a self-attention router and structurally heterogeneous experts yielded +1.6 mAP/+4.1 NDS improvements and 4× backbone FLOP savings(Xiang et al., 11 Aug 2025).
• Multi-modal object re-identification and fake news detection: Hierarchical decoupling and interaction-aware MoE gating produced robust, instance-adaptive fused features, supporting improved generalization across challenging domains (Wang et al., 14 Dec 2024, Liu et al., 21 Jan 2025).

6. Comparative Evaluation and Advantages

Hierarchical MoE fusion modules offer several practical and theoretical advantages:

• Selective specialization: By assigning distinct experts to varying feature depths or modalities, the architecture captures complementary patterns and avoids dilution of strong signals.
• Instance-wise adaptivity: Gating networks adapt the fusion logic for each sample, promoting robustness to noise and domain variation.
• Sparse and efficient computation: Top-K or top-1 gating enables computational savings, especially in inference scenarios where only a subset of experts is active (Xiang et al., 11 Aug 2025, Hao et al., 4 Sep 2025, Płotka et al., 8 Jul 2025).
• Superior generalization and interpretability: Empirical ablations attribute incremental gains to each fusion stage, validating the hierarchical decomposition and dynamic routing strategy (Cai et al., 16 Nov 2025, Płotka et al., 8 Jul 2025).

7. Research Directions and Open Problems

Key research questions remain regarding optimal expert granularity, gating network complexity, stability of hierarchical routing regimes, and generalization to new modalities or tasks. Further exploration of load-balancing strategies and scalable training methods is warranted, especially as expert set sizes and task heterogeneity increase. Comprehensive evaluation frameworks for hierarchical MoE architectures across domains with fundamentally different data properties will be critical to drive advances in adaptive fusion techniques.