Modular LLM Architectures

Updated 2 November 2025

Modular LLM architectures are design frameworks that decompose large language models into independently optimized modules for task-specific functions, ensuring specialization, scalability, and interpretability.
They employ strategies such as mixture-of-experts, role-based modularization, and hierarchical composition to achieve dynamic module selection and plug-and-play interoperability.
Applications span Verilog code generation, academic survey synthesis, text-to-video generation, and planning systems, offering targeted optimization and human-in-the-loop extensibility.

A modular LLM architecture is an approach in which the overall functionality of a LLM or LLM-based agent is cleanly factored into independently designed, implemented, and orchestrated submodules. Each module targets a well-defined subtask or competency, enabling targeted optimization, composable workflows, task-conditional specialization, and adaptive reuse across applications or domains. Modular architectures have become central to addressing the scalability, flexibility, interpretability, and robustness challenges that arise in contemporary LLM deployment.

1. Core Principles and Motivations

The modular approach in LLM architecture is motivated by several foundational objectives:

Specialization: Modules can be separately designed, fine-tuned, or even independently replaced for narrow or complex competencies. For example, distinct expert LLMs tailored for different Verilog circuit complexity levels in code generation (Nadimi et al., 11 Apr 2024), or plug-in camera control operators for dynamic video changes (Pan et al., 16 Apr 2025).
Separation of Concerns: Distinct cognitive or operational components—such as planning, reasoning, memory, perception, tool use—are implemented and evaluated independently (Shang et al., 8 Oct 2024, Yang et al., 1 Feb 2025, Zhang et al., 15 Jul 2025).
Compositionality: Modules can be orchestrated or recombined to build complex, hierarchical workflows, such as multi-agent literature survey generation, where pipeline stages are built from atomic MCP servers (Chao et al., 13 Oct 2025) or functional hybrid LLM–verifier loops for robust planning (Gundawar et al., 20 Nov 2024).
Human-in-the-Loop Extensibility: Modular boundaries define precise intervention points for user feedback, customization, or insertion of new modules tailored to new domains, communities, or modalities (Feng et al., 22 Jun 2024, Chao et al., 13 Oct 2025).
Scalability and Maintainability: Incremental extension, hot-swapping, or efficient pruning/finetuning of modules allows for robust scaling and maintenance as needs evolve (Shen et al., 2023).

These principles facilitate targeted optimization, system-wide transparency, and resilience against brittle monolithic model designs.

2. Design Patterns and Modularization Strategies

Contemporary research has advanced several modularization strategies, illustrated by the following architectures:

a. Mixture-of-Experts and Sparse Activation

Sparse Mixture-of-Experts (SMoE) models, such as ModuleFormer (Shen et al., 2023), implement modularization at the architectural level:

Expert “modules” (e.g., feedforward blocks, attention heads) are sparsely activated per input, determined by routing networks.
Stick-breaking attention mechanisms and specialized load-balancing losses enable emergent modularity from uncurated data.
The specialization is monitored and enforced via mutual information maximization and entropy-based pruning.

b. Multi-Expert and Role-Based Modularization

Task-oriented modularization, as realized in MEV-LLM (Nadimi et al., 11 Apr 2024), entails separate LLMs (experts) for discrete complexity segments (e.g., “basic,” “intermediate,” etc.) with a classifier gatekeeper. Each expert is trained only for its scope, providing both accuracy and interpretability.

c. Modular Reasoning and Agentic Decomposition

AgentSquare (Shang et al., 8 Oct 2024) and CapaBench (Yang et al., 1 Feb 2025) decompose LLM agents into explicit modules: planning, reasoning, tool use, memory, and reflection. Each module exposes a uniform IO interface; agent construction involves combinatorial search/evolution over module pools for optimal assembly per task.

d. Functional and Workflow Modularization

Compound LLM architectures (e.g., LLM-Modulo (Gundawar et al., 20 Nov 2024)) pair a solution-generating LLM with frequency- or context-triggered “verifier modules” (critics) that vet and, if necessary, reject and guide revisions of outputs, guaranteeing factual or structural correctness.

e. Modular Multimodal/Multilingual Orchestration

Multi-LLM systems at the edge or for pluralistic alignment (Luo et al., 1 Jul 2025, Feng et al., 22 Jun 2024) treat each LLM as a plug-and-play component for a specific modality, language, or community; their collaboration is coordinated by consensus/voting, fusion layers, or workflow routers.

3. Orchestration, Plug-and-Play, and Modularity at Inference

A hallmark of modular LLM architectures is the orchestration logic that dynamically composes and dispatches modules based on input, task, or runtime context.

Dynamic Module Selection: Inputs are parsed (possibly with LLM-based directors or classifiers) and routed to appropriate modules; e.g., user prompts decomposed into scene/action pairs by LLM-Director in Modular-Cam (Pan et al., 16 Apr 2025), or context-driven sub-agent invocation in penetration testing (Huang et al., 16 Sep 2025).
Composable “Operators” or Plug-ins: Modules such as CamOperator (motion control) or AdaControlNet (scene smoothing) (Pan et al., 16 Apr 2025) are plug-and-play; multiple can be combined without retraining, offering compositional control granularity.
Hierarchical/Recursive Invocation: High-level planning agents can recursively or hierarchically assemble sequences of modules (MCP servers or agent stages in LLM×MapReduce-V3 (Chao et al., 13 Oct 2025); sketch-based trees in hierarchical lifelong learning (Deng et al., 2021)).
Module Evolution and Recombination: AgentSquare (Shang et al., 8 Oct 2024) employs learned module programmers and combinators to synthesize and recombine new modules, rigorously searching the agent design space for optimal task performance.

Plug-and-play interoperability is reinforced through explicit IO interface standards and function-calling protocols, e.g., MCP (Chao et al., 13 Oct 2025) or intra-module APIs in LLM app layers (Hou et al., 6 Mar 2025).

4. Functional Properties, Evaluation, and Performance Attribution

Empirical studies stress that effective modularity is not merely nominal decomposition, but requires targeted design of functional scaffolding and robust evaluation:

Functional Capabilities: Key capabilities for modular agent performance include global context memory, inter-agent messaging, context-conditioned invocation, adaptive planning, and real-time monitoring (Huang et al., 16 Sep 2025). Their absence can result in context fragmentation, logic gaps, and poor coordination.
Empirical Gains: Properly augmented modular agents (e.g., PenHeal in penetration testing) significantly outperform both single-agent and naive modular baselines, with cumulative ablations reflecting the additive value of each scaffolding property.
Attribution and Synergy: Game-theoretic evaluation (Shapley Value (Yang et al., 1 Feb 2025)) provides principled, quantitative attribution of each module’s marginal contribution (and its interaction with others), enabling optimal module selection and best-of-breed agent engineering.
Benchmarking: Realistic, multi-domain datasets probe not only individual module effects but also coalition and synergy patterns, driving interpretability and targeted optimization.

5. Implementation, Extension, and Future Directions

a. Implementation Paradigms

Adapter-Based Extension: New task or domain capabilities are often integrated as LoRA adapters (e.g., CamOperator (Pan et al., 16 Apr 2025), LLM-ACTR (Wu et al., 17 Aug 2024)).
Discrete-to-Neural Bridging: Hybrid neuro-symbolic modules inject interpretable decision traces (e.g., extracted from ACT-R cognitive architectures) into LLMs for grounded, explainable decision-making (Wu et al., 17 Aug 2024).
Edge and Distributed Environments: Lightweight, federated, or privacy-preserving modules orchestrated through dynamic scheduling, blockchain-driven consensus, or split/federated learning, especially at the edge (Luo et al., 1 Jul 2025).

b. Scalability, Trust, and Security

Efficient Specialization: Sparse activation, expert pruning, and incremental module addition (Shen et al., 2023) enable efficient scaling and task specialization without catastrophic forgetting.
Gobally Trusted Modular Systems: Decentralized and consensus-based orchestration for robust, tamper-resistant multi-LLM ensembles, supporting on-chain auditing and privacy (Luo et al., 1 Jul 2025).
Human-in-the-Loop and Intervenability: Modular interfaces expose clear levers for intervention, feedback, and policy enforcement (Chao et al., 13 Oct 2025, Feng et al., 22 Jun 2024).

c. Research Challenges

Neural/Symbolic Integration: Seamless, differentiable interfaces between continuous and discrete modules, and coherent joint optimization (Wang et al., 28 Apr 2025).
Benchmarking Modular Reasoning and Safety: New metrics and evaluation protocols assessing interpretability, modular reasoning quality, and robustness.
Dynamic, Adaptive Module Composition: On-demand module instantiation, runtime adaptation to user needs, or environmental cues for agency and general intelligence.

6. Theoretical Guarantees and Hierarchical Lifelong Learning

Several modular architectures provide provable learning guarantees:

Hierarchical Learning & Program Induction: Sketch-based modular architectures can provably learn task hierarchies constructed as programs calling previously learned modules as subroutines (Deng et al., 2021). Task decomposition, context-based routing, and recursive composition yield efficient, scalable learning even for tasks that are intractable end-to-end.
Automatic Task Discovery: Decision tree-based routing or dynamic context extraction enables the agent to autonomously instantiate and wire new modules for previously unencountered tasks.

Theoretical frameworks clarify the quantifiable advantages of modularity for continual learning, compositional generalization, and efficient transfer.

7. Applications and Impact

Modular LLM architectures have demonstrated impact in various domains:

Application Area	Modularization Benefits
Text-to-video generation	Scene-action decomposition, plug-in camera control
Verilog code generation	Complexity-specialized expert modules
Academic survey generation	Hierarchically composed agent servers (MCP)
Gaming agents	Plug-and-play perception, memory, and reasoning
Planning/scheduling (reasoning)	Guaranteeing correctness via LLM/critic separation
Pluralistic alignment	Black-box collaboration with specialty LMs
Edge AI and multimodal inference	Decentralized, resource-efficient multi-LLM orchestration

These advances collectively bridge the gap between rigid, monolithic statistical models and robust, transparent, flexible AI systems. Modular architectures yield tangible gains in interpretability, controllability, incremental extensibility, and system-wide performance—establishing themselves as a foundation for the next generation of human-aligned, general, and trustworthy LLM-based AI.