LLM Modules: Architecture and Insights

• LLM Modules are distinct, composable components that decouple retrieval, rewriting, generation, memory, and safety functions in large language models.
• They streamline performance by enabling plug-and-play module replacement, reducing hallucinations and supporting domain adaptation.
• Frameworks like RETA-LLM, AIOS, and RoleRAG exemplify modular architectures that optimize resource management and enhance explainability.

LLM modules represent independently functioning, composable components within a complex LLM-based system. These modules, both at the software and architectural level, enable fine-grained separation of concerns, improve controllability, and facilitate flexible integration of external knowledge, tools, memory, and safety controls. Modularization in LLM systems has emerged as a response to challenges such as hallucination reduction, domain adaptation, end-to-end pipeline optimization, multi-role orchestration, and the enforcement of trustworthy behavior in multi-agent deployments.

1. Modular LLM Architectures: Definitions and Rationale

LLM modules are distinct, task-specific components (e.g., retrieval, rewriting, answer generation, memory, tool integration, planning, safety) designed to interact via well-defined interfaces. Modular design enables decoupling of key functions and extensibility for advanced LLM applications. For instance, RETA-LLM separates retrieval, passage extraction, answer generation, and fact checking, while frameworks such as AIOS and Teola further decompose runtime and scheduling functionalities into modular operating system-like elements (Liu et al., 2023, Mei et al., 25 Mar 2024, Tan et al., 29 Jun 2024). In agent settings, modules such as brain, memory, and tool interface support internal logic and interaction with external APIs or users (Yu et al., 12 Mar 2025, Hassouna et al., 17 Sep 2024).

The adoption of LLM modules is motivated by:

• Reducing hallucinations via explicit evidence injection and reference checking.
• Supporting domain adaptation by integrating bespoke data sources or IR engines.
• Enabling explicit pipeline control and resource allocation for large-scale deployments.
• Enhancing transparency and explainability in system design and evaluation.

2. Module Types and Pipelines: Key Framework Implementations

LLM module frameworks typically instantiate pipelines consisting of specialized modules. Table 1 summarizes canonical module sets in representative frameworks.

Framework	Modules Included	Primary Role
RETA-LLM	Request rewriting, document retrieval, passage extraction, answer generation, fact checking	Retrieval-augmented answer generation
RoleRAG	Query decomposition, retrieval judgment, sub-answer generation, summarization, new query generation, final synthesis	Unified RAG/QA with query graph control
AIOS	Scheduler, context manager, memory manager, storage manager, tool manager, access manager	Agent OS: resource and capability mgmt
TrustAgent	Brain, memory, tool, agent-agent, environment, user interaction	Trustworthy LLM agent decomposition

In RETA-LLM (Liu et al., 2023), modules function as a linear pipeline for retrieval-augmented answer generation, where each module boundary can be reconfigured (e.g., by swapping retrievers or fact checkers). The RoleRAG framework (Zhu et al., 21 May 2025) uses role-specific token optimization to multiplex tasks over a single LLM, creating soft modularization by tuning the token embeddings used to select the module behavior dynamically.

Teola (Tan et al., 29 Jun 2024) emphasizes a fine-grained primitive-based approach, decomposing even standard LLM inference into primitives like prefilling and decoding, yielding an execution graph that reveals parallelism and pipelining opportunities for optimized deployment.

Agent frameworks and trustworthy LLM surveys (Yu et al., 12 Mar 2025, Hassouna et al., 17 Sep 2024) decompose agents into brain, memory, tool, profile, planning, and security modules, among others, enabling separately targeted security, evaluation, and enhancement strategies.

3. Technical Implementation and Optimization of LLM Modules

Implementing LLM modules requires careful definition of module boundaries and workflow orchestration strategies. Key methodologies include:

• Loose Coupling of Retrieval and Generation: RETA-LLM employs a plug-and-play design where document retrieval (via dense or sparse retrievers) is fully separable from answer generation and verification, allowing dense or sparse index variants and alternative LLMs for each stage (Liu et al., 2023).
• Role-specific Soft Prompting: RoleRAG introduces only new token embeddings—keeping the base model weights frozen—to activate different module behaviors, described mathematically as:

$$p = \prod_{i=1}^m p_{\theta, \delta}(y_i^T | X^T; t_1; ...; t_n; y_{<i}^T)$$

where $\delta$ are small trainable embeddings that function as a modular switch (Zhu et al., 21 May 2025).

• Fine-Grained Task Graphs: Teola exposes a large workflow optimization space using primitive-level dataflow graphs, permitting parallel scheduling, pipelined execution, and cross-module batching to minimize end-to-end latency (Tan et al., 29 Jun 2024).
• Resource and Context Management: The AIOS framework organizes resource scheduling, memory, access, and external tool management into submodules, supporting both FIFO and round-robin scheduling with quantified context-switch overhead:

$T^{RR}_i = T_i + \alpha$

for each request, where $\alpha$ is the context-switch cost (Mei et al., 25 Mar 2024).

The implementation of memory and storage modules often involves LRU/K-LRU eviction, trie-based compression for memory optimization, and persistent vector databases for knowledge recall.

4. Evaluation, Benchmarking, and Attribution of Module Contributions

Measurement of modular system efficacy often moves beyond global end-to-end performance to per-module contribution analysis. The CapaBench framework proposes Shapley Value-based attribution for modular LLM agents (Yang et al., 1 Feb 2025):

$$\phi_i(v) = \sum_{S \subseteq N \setminus \{i\}} \frac{|S|! (|N|-|S|-1)!}{|N|!} \left[v(S \cup \{i\}) - v(S)\right]$$

where $v(S)$ is the system's measured performance with only the set of modules $S$.

This approach supports:

• Identifying key modules driving improvements (e.g., planning/action modules for online shopping or theorem proving).
• Targeting high-value modules for further optimization.
• Quantifying module synergy or redundancy.

Benchmarks such as multi-domain QA datasets, code contests (pass@1 in CodeChain (Le et al., 2023)), and real-world agent task suites provide a basis for empirical comparison.

5. Applications and Practical Impact

LLM modules afford flexible integration in a range of application domains:

• Retrieval-Augmented QA: Modular pipelines such as RETA-LLM and RoleRAG (Liu et al., 2023, Zhu et al., 21 May 2025) have demonstrated substantial gains in factuality and efficiency, particularly for in-domain and multi-hop QA.
• Code Generation: CodeChain uses modular sub-task extraction, clustering, and self-revision to elevate both correctness and modularity of output code; relative pass@1 improves by up to 76% on CodeContests (Le et al., 2023).
• Agent and OS-level Deployments: AIOS and LLM-Agent-UMF (Mei et al., 25 Mar 2024, Hassouna et al., 17 Sep 2024) modularize resource, tool, and access management for scalable intelligent agent deployment.
• Medical and Industrial Pipelines: End-to-end module chains—in ASR-LLM medical diagnosis (Kumar, 18 Feb 2025) or code module retrieval in robotics firmware (Arasteh et al., 5 Mar 2025)—highlight the generalizability of the modular approach.
• Data Preparation and Curation: Modular toolkits such as Data Prep Kit (Wood et al., 26 Sep 2024) offer scalable transform-based modules for large-scale LLM training data processing.

6. Challenges, Limitations, and Future Research Directions

Despite the flexibility of LLM modules, several technical limitations and open questions persist:

• Module Boundary Design: Selecting optimal granularity (fine- vs. coarse-grained) affects both system complexity and optimization potential (Tan et al., 29 Jun 2024).
• Trustworthiness and Security: Isolating memory and tool modules aids targeted defenses, but attack surface area may increase; advances in sandboxing, memory integrity checks, and multi-agent collaboration are needed (Yu et al., 12 Mar 2025).
• Dynamic Scheduling and Adaptation: Most current frameworks assume static or pre-defined graphs; dynamic adaptation to real-time requirements, user input, or environmental feedback will require more sophisticated runtime orchestration (Tan et al., 29 Jun 2024).
• Evaluation Complexity: Shapley-based attribution is computationally expensive for large numbers of modules, and ground truth for module utility may be ambiguous in open-ended tasks (Yang et al., 1 Feb 2025).
• Standardization: Lack of consensus on module interfaces and definitions can impede interoperability and extension across frameworks or research groups (Hassouna et al., 17 Sep 2024).

Future work is likely to focus on dynamic adaptive graphs, richer interfaces for tool and memory modules, integrated trustworthiness guarantees, and more general frameworks for module benchmarking and optimization.

7. Conclusion

LLM modules underpin a new generation of LLM systems capable of more reliable, interpretable, scalable, and domain-adaptable operation across a broad spectrum of applications. The integration of clearly bounded, specialized modules—supported by frameworks such as RETA-LLM, Teola, RoleRAG, AIOS, and TrustAgent—enables explicit control over the interaction between retrieval, generation, resource management, safety, planning, and domain-specific reasoning. Empirical evidence demonstrates that this modular paradigm not only enhances factual consistency and efficiency but also paves the way for fine-grained attribution, explainability, and robust engineering of trustworthy AI agents (Liu et al., 2023, Mei et al., 25 Mar 2024, Tan et al., 29 Jun 2024, Yang et al., 1 Feb 2025, Yu et al., 12 Mar 2025).