Modular Personalization in AI Systems

• Modular personalization is an approach that decomposes user experience, model internals, adaptivity logic, or system components into independent, composable modules.
• It enables fine-grained control and scalable adaptation across various applications including LLMs, federated learning, robotics, and wearable technology.
• Empirical evaluations show improved personalization scores, efficiency gains, and reduced computational overhead compared to monolithic systems.

Modular personalization denotes the architectural and methodological paradigm in which systemic personalization is achieved by decomposing the user experience, model internals, adaptivity logic, or physical structure into discrete, composable modules—each independently configurable, learnable, or replaceable. This approach contrasts sharply with monolithic personalization strategies, enabling fine-grained control, scalability, collaborative sharing, and robust adaptation, from LLM agents to federated models, multimodal planners, robotic systems, wearable technologies, and haptic devices.

1. Core Principles and Formalizations

Modular personalization fundamentally relies on decomposing personalization-related processes into interoperable components, each with formal interfaces and explicit optimization goals. In LLM agents, MAPLE exemplifies this by orthogonally decomposing “memory,” “learning,” and “personalization” into independent sub-agents (Piskala, 3 Feb 2026):

• Memory ($\mathcal{M}$): Pure storage/retrieval; $$\mathcal{M} = (\mathcal{M}_T, \mathcal{M}_Z, \mathcal{M}_\theta)$$ for token records, embeddings, and adapters.
• Learning ($\mathcal{L}$): Asynchronous insight extraction, e.g., $I_u = \mathcal{L}(E_u;\theta_\mathcal{L})$.
• Personalization ($\mathcal{P}$): Real-time context composition and adaptation using outputs of $\mathcal{M}$ and $\mathcal{L}$, e.g., $P(q, u) = \mathrm{COMPOSE}(C_u,q)$.

In personalized federated learning, modular personalization is realized by assembling per-client model architectures from a global module pool, with client-specific routing (Wang et al., 2022). Each client selects active modules according to its own data distribution, using a hypernetwork for distribution-aware modular assembly:

• FedMN: Client $m$ uses a binary mask $V_m$ drawn from a Bernoulli distribution $$\mathcal{M} = (\mathcal{M}_T, \mathcal{M}_Z, \mathcal{M}_\theta)$$0, parameterized by dataset embeddings.
• ModFL: Splits models into configuration and operation modules, federating each within homogeneous cohorts to isolate device and data heterogeneity (Liang et al., 2022).

For parameter-efficient tuning of LLMs, modular personalization is operationalized by decomposing adapters (e.g., LoRA) into “pieces” that can be pooled and recombined, as in Personalized Pieces (Per-Pcs) (Tan et al., 2024).

2. Architectures and Modularization Strategies

The implementation of modular personalization spans several architectural strategies:

• Sub-agent and sub-component decomposition: As in MAPLE, each agentic function (memory, learning, personalization) is encapsulated in a sub-agent with a rigorously defined API, operating timescale, and optimization target (Piskala, 3 Feb 2026).
• Module pools with dynamic routing: Federated learning frameworks such as FedMN and ModFL assemble per-client networks from a shared block pool, using hypernetworks or cohort-based aggregation to personalize architecture and learning, thereby achieving both communication efficiency and robust performance under non-IID distributions (Wang et al., 2022, Liang et al., 2022).
• Modular adapters and per-layer assembly: Per-Pcs decomposes LoRA adapters into layerwise pieces, with each piece associated with a lightweight gate function and pooled across users. Target users assemble personalized adapters from this pool via compatible activation scores, drastically reducing storage and compute costs (Tan et al., 2024).
• Modular pipeline orchestration: Agent WARPP demonstrates parallel orchestration of modular agents in task-oriented dialogue, with a runtime personalizer agent that prunes workflows and toolset per user attributes, rather than enforcing monolithic branching logic (Mazzolenis et al., 23 Jul 2025).
• Plug-and-play and composable hardware units: In personalized trip planning (Liu et al., 2019), modular ingestion of auxiliary data, preference modules, constraint modules, and search engines allows arbitrary user customization via easy module addition. In modular robots (Chen et al., 15 Sep 2025, Chen et al., 23 Feb 2026), physical modules for form and function are openly configured and replaced across the lifespan.

3. Personalization Mechanisms and Algorithms

Modular personalization enables flexible, scalable, and robust personalization mechanisms through:

• Retrieval and gating: Selecting memory entries or adapter pieces via recency/confidence-weighted scoring (MAPLE) or via learned compatibility gates (Per-Pcs).
• Routing networks/hypernetworks: In FedMN, routing hypernetworks parametrically select module masks based on dataset embeddings, permitting continuous adaptation to each client's data distribution (Wang et al., 2022).
• Information-theoretic optimization: Modular haptic displays personalize hardware configuration by maximizing mutual information between intended message and user perception, modeling user preferences and sensitivities in the selection of hardware modules and signal forms (Valdivia et al., 6 Jun 2025).
• Bandit and adaptive policies: The MOOClet formalism embeds experimentation and personalization into a single modular selection function, permitting dynamic adaptation and exploration/exploitation tradeoff by updating selection policies based on randomized experiments and observed outcomes (Williams et al., 2015).

4. Evaluation Criteria and Empirical Performance

Quantitative and qualitative evaluation of modular personalization frameworks is carried out via:

• Task-specific accuracy/fidelity: MAPLE achieves a 14.6% improvement in personalization score and a trait incorporation rate increase from 45% to 75% over stateless baselines on held-out persona benchmarks (Piskala, 3 Feb 2026).
• Efficiency metrics: Per-Pcs achieves equivalent or superior performance to full-parameter PEFTs with 38× lower storage and 7× lower compute costs, as well as 99.3% of the utility of strong baselines (Tan et al., 2024); FedMN achieves up to 80% reduction in communicated parameters (Wang et al., 2022).
• User-centric studies and human factors: Modular robotics frameworks measure breadth, depth, and agency in user-chosen module configurations, as well as repairability-in-use, expressive adequacy, and lifespan plausibility in co-design workshops (Chen et al., 15 Sep 2025, Chen et al., 23 Feb 2026).
• Statistical validation: MAPLE and WARPP report robust gains confirmed by statistical tests such as Welch’s t-test and large Cohen's d; modular haptic interfaces report significant differences in performance and preferences across hardware configurations based on repeated-measures ANOVA (Valdivia et al., 6 Jun 2025).
• Scalability and robustness: Modular approaches remain robust with varying pool sizes, sharing ratios, and user activity levels (Per-Pcs, FedMN), and avoid computational bottlenecks by isolating adaptations to subcomponents.

5. Domains and Applications

Modular personalization supports diverse applications:

• Agentic AI systems: Through principled decomposition of memory, learning, and personalization, agents obtain dynamic, robust user adaptation without sacrificing latency or interpretability (Piskala, 3 Feb 2026).
• Federated learning: Modular networks and adapters efficiently support client heterogeneity and privacy, avoiding the model proliferation or storage explosion of naïvely personalized per-user models (Wang et al., 2022, Liang et al., 2022, Tan et al., 2024).
• Personalized generation and editing: Modular concept token routing and explicit knowledge replay in unified LMMs (OmniPersona) permit consistent personalized understanding, generation, and editing (Zhong et al., 11 Jan 2026); modular encode–then–route pipelines in AnyStory enable single/multi-subject text-to-image personalization with strong identity and text fidelity (He et al., 16 Jan 2025).
• Interactive and embodied systems: Modular, physically reconfigurable platforms in robotics (Chen et al., 15 Sep 2025, Chen et al., 23 Feb 2026), wearable electronics (Pau, 2017), and haptic interfaces (Valdivia et al., 6 Jun 2025) enable both function and form to be adapted per user need, physical context, or lifespan phase.
• Personalized decision/planning systems: Modular trip planners ingest arbitrary user-defined data and preferences as modules, supporting complex multi-modal planning with user-defined constraints (Liu et al., 2019).

6. Limitations, Challenges, and Future Directions

Key challenges identified in the literature include:

• Policy/model selection and interpretability: As modular pools grow (e.g., adapter pieces, module blocks), retrieval and gating efficiency may require new indexing structures (Tan et al., 2024), and interpretability of gates/routing policies remains an open problem.
• Complexity and overfitting control: Excessive modularity risks overfitting and computational overhead; efforts such as per-user gates or constrained module selection (top-$$\mathcal{M} = (\mathcal{M}_T, \mathcal{M}_Z, \mathcal{M}_\theta)$$1) are employed to mitigate these effects (Tan et al., 2024, Wang et al., 2022).
• Human-centered evaluation: In embodied and social systems, evaluating expressive adequacy, repairability, and emotional sustainability of modular configurations requires longitudinal, qualitative, and scenario-based studies (Chen et al., 15 Sep 2025, Chen et al., 23 Feb 2026).
• Robustness to heterogeneity: Highly heterogeneous clients/data may require dynamic adjustment of module pools or more flexible module structures (e.g., hierarchical or graph-structured module sets) (Wang et al., 2022).
• Open, community-extensible standards: Fabrication-aware, open-source module design and governance remain future goals in robotics and wearables, balancing extensibility and safety (Chen et al., 23 Feb 2026, Pau, 2017).
• Automated discovery and continual adaptation: Modular formalisms such as the MOOClet pattern (Williams et al., 2015) suggest an ongoing loop between experimentation and personalization; future frameworks may require scalable continual learning pipelines for both statistical and design-driven domains.

7. Representative Systems and Comparative Features

System/Paper	Modularization Level	Personalization Mechanism	Evaluation Highlights
MAPLE (Piskala, 3 Feb 2026)	Memory, Learning, Pers. subagents	Memory+insight retrieval, prompt assembly	+14.6% pers. score, 75% trait rate
FedMN (Wang et al., 2022)	Module pool, client routing	Hypernetwork-guided block selection	+80% comm. efficiency, top accuracy
Per-Pcs (Tan et al., 2024)	Adapter pieces (LoRA, etc.)	Gate-scored pooling, on-demand assembly	38× lower storage, robust/competitive
WARPP (Mazzolenis et al., 23 Jul 2025)	Agentic parallel modules	Runtime workflow/tool pruning	3× efficiency, +8pt F1 vs. baselines
ModFL (Liang et al., 2022)	Config/operation module split	Cohort-based aggregation	+7–15pp acc. on non-IID, robust in IID
OmniPersona (Zhong et al., 11 Jan 2026)	Decoupled concept tokens	Task-specific routing, knowledge replay	+17.9% editing score vs. GPT-4o
AnyStory (He et al., 16 Jan 2025)	Encode–then–route (router, encoders)	Routing mask, identity-aware injection	+12% identity match, 0.72 IoU
Modular robots (Chen et al., 15 Sep 2025, Chen et al., 23 Feb 2026)	Physical slot/module architecture	User-configured at design/use time	Co-designed adaptability, sustainability
MOOClet formalism (Williams et al., 2015)	Component-level, policy modularity	Experimental/personalized dispatcher	+50% email response rate via policy switch

Collectively, these systems demonstrate that modular personalization is a scalable, robust, and adaptive solution for tailoring complex systems to diverse user needs, interaction histories, embodiments, and operational contexts.