Modular Experts in Neural Systems

• Modular experts are specialized neural modules within mixture-of-experts systems that provide targeted computation for specific tasks.
• They employ conditional routing and diverse gating strategies to selectively activate expert components based on the input.
• Their design enhances scalability, rapid adaptation, and interpretability while mitigating catastrophic forgetting and resource inefficiency.

A modular expert is a specialized, self-contained neural module in a larger architecture—most often a mixture-of-experts (MoE) system—where experts are designed, trained, and managed to provide targeted computational specialization for particular domains, tasks, modalities, or sub-functions. Instead of treating all parameters as monolithic, modular experts enable architectures that flexibly and efficiently route computation, adapt, and scale, by activating only relevant components conditionally. Recent advances demonstrate modular experts in vision, language, robotics, reasoning, and online prediction settings, with diverse gating and assignment strategies. Modularization enables improved accuracy, sample-efficiency, rapid adaptation, catastrophic forgetting mitigation, interpretability, and computational/resource efficiency across a range of neural and algorithmic systems.

1. Fundamental Principles and Taxonomy

Modular experts are instantiated as independently parameterized neural networks or sub-networks, typically with architectures tailored to the target domain or functional role. The broader architecture is composed by arranging these experts in parallel or sequentially, with a router or gating network to determine expert allocation for each input.

Core design principles:

• Conditional computation: Only a subset of experts is activated per input, enabling scalable parameter capacity without a proportional compute penalty (Lo et al., 2024).
• Specialization: Each expert focuses on a subset of tasks, data distributions, or representations, often yielding higher per-task performance (Schafhalter et al., 2024).
• Composability: Experts can be added, removed, or recomposed with minimal (or no) retraining, facilitating continual learning, model surgery, and system extension (Gururangan et al., 2021, Zhang et al., 31 Jan 2025).
• Routing and gating: Separate mechanisms—ranging from trainable routers, fixed caps, heuristic rules, or text-based matchers—determine expert assignment for each sample or token (Kuzmenko et al., 2 Jul 2025, Xiang et al., 11 Aug 2025, Jain et al., 2024).

A common taxonomy of modular expert frameworks divides them along:

• Parameter sharing: Disjoint (non-overlapping) vs conditionally overlapping (COMET-style) vs side-by-side (adapters, LoRA, etc.) (Shaier et al., 2024).
• Routing mechanism: Trainable gates (softmax, attention, hard top-k, etc.), fixed projection/caps, task-ID-based, external classifier, or semantic matching (Lo et al., 2024, Jain et al., 2024).
• Granularity of modularity: Layer-level (sparse MoE FFN), block-level (DEMIX full FFN blocks), or functional module (entire backbone swaps, as in CBDES MoE (Xiang et al., 11 Aug 2025)).

2. Gating and Routing Strategies

Expert assignment is mediated through a gating or router function, with the choice of strategy impacting expressivity, efficiency, and specialization:

• Trainable routers: Softmax or sparse top-k gates compute input-dependent mixture weights for each expert (Krishnamurthy et al., 2023, Lo et al., 2024, Chen et al., 2022, Pandey et al., 28 Jun 2025). These support fine-grained adaptation and can be coupled with regularizers penalizing expert imbalance or entropy.
• Attention-based gating: The router utilizes representations from the experts themselves to compute assignment scores—for instance, via dot-product attention, sharpening the semantic partitioning among experts (Krishnamurthy et al., 2023).
• Fixed or biologically-inspired routing: Determined by fixed random projections and k-WTA caps, producing an exponential space of overlapping “implicit” experts without the risk of gate collapse and with strong alignment to input similarity (Shaier et al., 2024).
• External or textual routers: Especially for modular robotics and multi-expert serving, an independent external router selects among experts based on meta-descriptions, embeddings, or prompt-based inference, fully decoupled from the expert internals (Kuzmenko et al., 2 Jul 2025, Jain et al., 2024).
• Specialized token-level or structured routers: E.g., in multi-task transformers, the routing is per-token or per-modality, parameterized by noised MLPs combined with mutual information losses to enforce expert-task coupling (Chen et al., 2022).

Load-balancing and diversity-inducing losses (e.g., auxiliary importance/load regularizers) are essential in trainable-gate settings to prevent expert collapse and ensure full capacity utilization (Lo et al., 2024, Krishnamurthy et al., 2023, Pandey et al., 28 Jun 2025).

3. Architectural Variants and Modularization Schemes

Modular expert systems manifest in multiple architectural paradigms:

• Sparse MoE layers: Each layer comprises N experts, with a softmax router selecting top-k per input. Most widely used in LLMs to scale parameter counts while saving FLOPs (Lo et al., 2024, Krishnamurthy et al., 2023).
• Parallel block-wise experts: MoDE, DEMix, and related systems run full transformer blocks in parallel to the backbone, each expert being a multi-layer transformer (not just FFN), routed via lightweight gates (Schafhalter et al., 2024, Gururangan et al., 2021).
• Functional module-level experts: Experts replace entire backbone modules (e.g., camera encoder in BEVFusion for autonomous driving), selected via input-aware self-attention routers for interpretably diverse computation (Xiang et al., 11 Aug 2025).
• Compound expert-serving frameworks: CoE and BTS frameworks orchestrate independent expert LLMs with a lightweight router and “stitch” layers, supporting dynamic aggregation, addition, and removal of experts in serving or continual learning flows (Jain et al., 2024, Zhang et al., 31 Jan 2025).
• Heterogeneous experts: Instead of homogeneous units, Hecto employs structurally distinct experts (e.g., GRU for temporal, FFNN for static reasoning) for functional specialization at low expert count (Pandey et al., 28 Jun 2025).
• Fixed-overlap modularity: COMET replaces gating with random projection + k-WTA, resulting in an exponential number of overlapping expert subsets per input, yielding faster, robust adaptation and improved regularization (Shaier et al., 2024).
• Task-aware modularity: Mod-Squad attaches per-task routers and uses mutual information regularization to extract compact per-task subnetworks after training, maintaining both cooperation and specialization (Chen et al., 2022).
• Depth-specialized modularity: DS-MoE routes input dynamically to variable-depth “reasoning chains” of specialized modules (shallow, compositional, inference, memory, meta-cognitive), matching the complexity of each case (Roy et al., 24 Sep 2025).

4. Training, Regularization, and Expert Composition

Training modular experts generally combines single-task or domain-specific pretraining of each expert, followed by a composition or integration phase:

• Independent expert pretraining: Each expert is trained on its own domain/task data, freezing the backbone or non-expert layers (Schafhalter et al., 2024, Gururangan et al., 2021, Kuzmenko et al., 2 Jul 2025). In KD-MoE, experts distill knowledge from multilingual or multi-domain teachers (Al-Maamari et al., 2024).
• Joint optimization: Experts and routers (and optionally adaptors) are co-trained over the union of tasks, leveraging standard task losses plus explicit regularization terms (load balance, entropy, mutual information, etc.) (Chen et al., 2022, Pandey et al., 28 Jun 2025).
• Compositional fine-tuning: Lightweight adaptation (e.g., gating networks, “stitch” layers) is trained over frozen experts to harmonize their representations and route information efficiently in a generalist or multi-task model (Zhang et al., 31 Jan 2025).
• Regularization: Mutual information maximization/sharpening encourages strong expert-task association; load/importance balancing prevents unused experts; entropy penalties force sparse, confident routing; specialty-preservation (Chen et al., 2022, Krishnamurthy et al., 2023, Pandey et al., 28 Jun 2025).

Knowledge distillation is used in some modular approaches to transfer competence from a single monolithic teacher to a bank of specialized students (Al-Maamari et al., 2024).

5. Empirical Properties: Specialization, Catastrophic Forgetting, and Interpretability

Recent studies confirm that modular experts yield robust specialization, resistance to interference/catastrophic forgetting, and improved interpretability:

• Specialization and task partitioning: Gating and mutual information regularization (as in Mod-Squad, MIcro) align experts to distinct tasks or reasoning types. Interpretability is validated by causal ablation: removing a module correlated with drops in its domain's performance (AlKhamissi et al., 16 Jun 2025, Chen et al., 2022).
• Catastrophic forgetting mitigation: MoE/Modular systems that train new experts for each domain—as in DECmix, MoDE, and KD-MoE—display zero or near-zero catastrophic forgetting, outperforming sequential or joint fine-tuning (Gururangan et al., 2021, Schafhalter et al., 2024, Al-Maamari et al., 2024).
• Extensibility and surgical modification: New experts can be added (“add a domain block,” “branch and stitch,” “COMET mask expansion”) by freezing all other modules, while unwanted experts can be removed at test time with minimal performance loss elsewhere (Zhang et al., 31 Jan 2025, Gururangan et al., 2021, Shaier et al., 2024).
• Interpretability via explicit routing: Systems such as DS-MoE or MiCRo inherit human-interpretable reasoning chains or explainable specialization, as each prediction is accompanied by an explicit pathway or chain of activated experts (Roy et al., 24 Sep 2025, AlKhamissi et al., 16 Jun 2025).
• Efficiency and resource scaling: By design, modular experts enable inference-time efficiency (conditional computation), flexible VRAM scaling, and, with external routers, only load/serve one expert per query (Jain et al., 2024, Kuzmenko et al., 2 Jul 2025).

6. Systems, Serving, and Practical Deployment

Real-world deployment of modular expert systems requires attention to serving, dynamic memory management, and resource allocation.

• Compound LLM serving: CoE and Robust-CoE demonstrate practical AI serving by routing per-prompt requests to the best expert, with memory architectures (e.g., SambaNova SN40L) that enable rapid parameter swapping and KV-cache sharing across experts (Jain et al., 2024).
• Robotics and decision systems: MoIRA leverages external text-based routers to orchestrate LoRA-modularized VLA experts, demonstrating superior specialization and low overhead for robotic manipulation and spatial tasks (Kuzmenko et al., 2 Jul 2025).
• Functional module encapsulation: In CBDES MoE, full backbones (e.g., Swin, ResNet, PVT) are swapped in as camera encoding experts, with a lightweight self-attention router, delivering ensemble-like performance at a fraction of the compute (Xiang et al., 11 Aug 2025).
• Low-rank adaptation and expert selection: LoRA-Mixer and MoDE integrate specialized, task-adapted low-rank modules (LoRA) or transformer blocks as experts, with serial routing for efficient and robust expert coordination in multi-task and few-shot adaptation (Li et al., 17 Jun 2025, Schafhalter et al., 2024).
• Fixed or text-based routing: COMET shows that, by abandoning trainable gates, routing can be made stable and efficient in both training and deployment, essential for continual and scalable systems (Shaier et al., 2024).

7. Limitations, Open Questions, and Future Directions

While modular experts offer substantial advantages, several challenges remain:

• Expert collapse and under-utilization: Without careful routing and balancing losses, MoE systems can degenerate into a few experts being heavily used, wasting capacity (Krishnamurthy et al., 2023, Lo et al., 2024).
• Designing expert granularity and diversity: It remains open how best to set expert specialization levels, overlap degree, and architectural heterogeneity to match data/task heterogeneity.
• Scaling and memory efficiency: As expert banks grow, memory and parameter count scaling become bottlenecks, especially for full-model modularization (e.g., CoE routing among dozens of LLMs). Strategies based on dynamic loading, tiered memory, and sparse caching are active research areas (Jain et al., 2024).
• Routing generalization and robustness: External routers (embedding or LM-based) may be brittle to distribution shifts or semantic ambiguity in task meta-descriptions; reliability and fallback mechanisms are needed (Kuzmenko et al., 2 Jul 2025).
• Integration with continual and meta-learning: Modular experts align well with non-stationary, evolving, or federated environments; best practices for lifelong learning and online adaptation are emerging but still unsettled (Gururangan et al., 2021, Shaier et al., 2024).
• Provable guarantees: In online prediction, modular utility functions allow for precise analysis of regret, incentive compatibility, and computational efficiency—areas where modularity is particularly tractable (Sadeghi et al., 2023).

Ongoing work aims at unifying modular expert systems with multi-modal, multi-agent, and biologically grounded architectures, expanding their applicability and interpretability in both machine and human-inspired intelligence.

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References:

(Jain et al., 2024, Schafhalter et al., 2024, Gururangan et al., 2021, Chen et al., 2022, Krishnamurthy et al., 2023, Pandey et al., 28 Jun 2025, Li et al., 17 Jun 2025, Roy et al., 24 Sep 2025, Shaier et al., 2024, Lo et al., 2024, Kuzmenko et al., 2 Jul 2025, Xiang et al., 11 Aug 2025, Zhang et al., 31 Jan 2025, Al-Maamari et al., 2024, AlKhamissi et al., 16 Jun 2025, Sadeghi et al., 2023, AlKhamissi et al., 16 Jun 2025, Xiang et al., 11 Aug 2025, Kuzmenko et al., 2 Jul 2025).