Modular Memory Systems

• Modular memory systems are architectures that decompose memory functions into independently tuned modules for data storage, retrieval, and management.
• They enable enhanced efficiency, scalability, and auditability through plug-and-play designs, pipeline decomposition, and robust fallback strategies.
• Empirical evaluations show that modular designs reduce token counts, improve task success, and support energy-efficient hardware implementations.

Modular memory systems are defined as architectures in which memory functionality is decomposed into distinct, auditable, and independently tunable modules, each responsible for a well-scoped aspect of storage, transformation, retrieval, management, or reasoning over data. These systems span cognitive architectures, machine learning agents, conversational AI, hardware accelerators, and quantum information processing. Modularization is often motivated by the need for controllability, extensibility, efficient scaling, and fine-grained performance optimization, as well as for rigorous support of analysis, audit, and adaptation in both algorithmic and architectural contexts.

1. Fundamental Principles of Modularity in Memory Systems

Modular memory systems achieve separation of concerns by abstracting memory functionality into clearly defined phases, interfaces, or modules, each with well-specified inputs, outputs, and behavioral guarantees. This principle enables:

• Independent tuning and replacement: Each module (e.g., encoding, verification, retrieval) can be swapped, tuned, or audited without affecting others.
• Explicit auditability: Intermediate representations and decision points are preserved and can be traced.
• Configurability and extensibility: Plug-and-play architectures facilitate rapid research and deployment of new memory techniques or storage backends.
• Robustness through redundancy and fallback: Modular designs can include safety nets such as fallback retrieval, reflection, and drift detection phases for resilience.

For example, MeVe’s five-phase pipeline disaggregates retrieval-augmented generation (RAG) into initial retrieval, cross-encoder-based verification, BM25 fallback, context prioritization, and token budgeting, allowing independent control of each phase and exposing clear hand-off points (Ottem, 1 Sep 2025). In agentic systems, MemEvolve formalizes memory architectures as four-tuple combinations of Encode, Store, Retrieve, and Manage modules, supporting both hand-engineered and meta-evolved memory strategies (Zhang et al., 21 Dec 2025).

2. Canonical Modular Architectures and Their Modules

A taxonomy of modular memory system modules, with representative instantiations, includes:

Module Type	Function	Example Instantiations / Algorithms
Encode	Convert raw inputs (e.g., trajectories, utterances) into structured, retrievable form	LLM-guided lesson extraction (Zhang et al., 21 Dec 2025), CLIP feature extraction (Kang et al., 8 Apr 2025)
Store	Persistently archive encoded items	Vector DB (Faiss, ChromaDB), JSON store, graph DB, hardware memory slices (Zhang et al., 21 Dec 2025, Braas et al., 13 Nov 2025, Asgari et al., 2018)
Retrieve	Select relevant memory units given query/state	kNN, cross-encoder scoring, semantic search, function matching, graph traversal
Manage	Abstract/prune/reflect on stored memory	Summarization, temporal compression, importance filtering, skill pruning
Verification	Assess and filter candidate memory items for relevance	Cross-encoder filtering, thresholding, contradiction detection
Compression	Reduce memory size by hierarchical or temporal summarization	Temporal Binary Compression, MemoryBank-like summarization trees (Xi et al., 12 Aug 2025, Zhang et al., 4 May 2025)
Fallback	Provide alternative retrieval path if primary strategy fails	BM25 fallback retrieval (Ottem, 1 Sep 2025)

The MeVe architecture (Ottem, 1 Sep 2025) epitomizes modular RAG, with strictly ordered, hand-off defined phases. MemEngine (Zhang et al., 4 May 2025) formalizes modular memory at three hierarchical levels: functions (e.g., embedder), operations (e.g., store, recall), and models (e.g., MemoryBank, Reflexion), each inheriting standard APIs. Modular memory systems for dialogue integrate skill routers dispatching to memory heads, e.g., BlenderBot3-M³ (Choi et al., 2023).

Hardware realizations, such as memory slices (Asgari et al., 2018) and in-memory computing for modular multiplication (Li et al., 5 Nov 2025), modularize the physical memory-compute substrate, mapping arithmetic workloads across slice or macro boundaries for scalable throughput.

3. Design Methodologies and Algorithms in Modular Systems

Construction of modular memory systems typically leverages the following methodologies:

• Pipeline decomposition: Strict phase ordering as in MeVe supports dataflow isolation and tractable auditing (Ottem, 1 Sep 2025).
• Plug-and-play mechanisms: Configuration files or registry patterns, as in MemEngine, allow the replacement of submodules (e.g., retrievers, summarizers) without interface changes (Zhang et al., 4 May 2025).
• Genotypic architecture search: MemEvolve encodes memory system designs as discrete four-tuples, evolving architectures via meta-evolutionary strategies that mutate, recombine, and select for compositional module improvements (Zhang et al., 21 Dec 2025).
• Reflection and management: Modules implementing context drift detection, summarization, coherence, and utility scoring (e.g., as in Contextual Memory Intelligence's Insight Layer (Wedel, 28 May 2025)) maintain longitudinal memory health and correct for error or obsolescence.
• Memory replacement and transfer: Memory-modular classification systems decouple world knowledge and task logic, enabling fine-grained replacement of memory contents without retraining the core reasoning model (Kang et al., 8 Apr 2025).

Mathematically, modular systems often formalize retrieval as

$$\mathcal{R}(M_t, s_t, \mathcal{Q}) = \arg\max_{x \in M_t} \mathrm{sim}(\phi(s_t, \mathcal{Q}), \psi(x))$$

and manage the update function as

$M_{t+1} = \mathcal{U}(M_t, e_t)$

with per-module operations defined and orchestrated according to system requirements.

4. Empirical Impact and Evaluation

The modular paradigm consistently yields improvements in efficiency, flexibility, and sometimes performance compared to monolithic or ad-hoc alternatives:

• MeVe demonstrated 57–75% reductions in retrieved token counts on Wikipedia and HotpotQA QA tasks relative to standard RAG with <0.2 s added latency per query, with ablation confirming each phase’s necessity for efficiency and robustness (Ottem, 1 Sep 2025).
• MemEvolve delivered up to +17% relative pass@1 gains on agentic benchmarks by evolving memory architectures, with robust generalization across both tasks and LLM backbones (Zhang et al., 21 Dec 2025).
• BlenderBot3-M³ achieved a 4% gain in overall F1 and a 40% reduction in memory size on multi-session chat, without adverse effect on any of the 67 supported conversational skills (Choi et al., 2023).
• LEGOMem, a modular procedural memory for multi-agent workflow, improved task success rates by 12–13% across agent team strength, reduced average execution steps by 16%, and lowered error rates by 18% compared to memory-free systems (Han et al., 6 Oct 2025).
• In hardware, memory slice architectures exhibited superlinear speedup and power efficiencies up to 747 GFLOPs/J for LSTM training by partitioning and mapping workload across slices (Asgari et al., 2018).
• Progressive memory compression in modular systems, as in Livia, cut average memory per user to 30% of baseline while maintaining 92% important-event recall and negligible (<0.6% CPU) overhead, supporting realistic user experience in personalization settings (Xi et al., 12 Aug 2025).

A plausible implication is that modular decomposition is compatible with, and often essential for, scaling and adaptation in both software and hardware memory systems.

5. Applications and Domain-Specific Instantiations

Modular memory systems have been adopted across a wide spectrum:

• LLM systems: Modular RAG (MeVe (Ottem, 1 Sep 2025)), agent memory architectures (MemEvolve (Zhang et al., 21 Dec 2025), MemEngine (Zhang et al., 4 May 2025)), memory-augmented dialogue (BlenderBot3-M³ (Choi et al., 2023), modular NPC systems (Braas et al., 13 Nov 2025)).
• Multi-agent and orchestration: LEGOMem supports precise role-aware allocation of full-task and subtask procedural memories to orchestrator and agent roles in workflow automation (Han et al., 6 Oct 2025).
• Compression and long-term memory: Livia employs temporal binary compression and dynamic importance filtering as modular agents to adaptively manage sizes and priorities in emotionally supportive AR companions (Xi et al., 12 Aug 2025).
• Image classification: Memory-modular learners achieve generalization to new image classes via replaceable memory modules holding external features, enabling zero/few-shot and incremental classification without retraining (Kang et al., 8 Apr 2025).
• Transactional memory: HeTM exposes a programmable, modular abstraction for CPU-GPU transactional memory, hiding hardware and software heterogeneity under a uniform STM/HTM interface (Castro et al., 2019).
• Hardware systems: Memory slice architectures and in-memory modular multiplication macros realize scalable, energy-efficient, and highly parallel memory-compute fabrics, adaptable via varying numbers or types of modules (Asgari et al., 2018, Li et al., 5 Nov 2025).
• Quantum information: Distributed QLSTM with modular quantum subcircuits supports scalable sequence modeling across networked quantum processing units (Chen et al., 18 Mar 2025), and modular quantum memories enable polynomially efficient multipartite entanglement generation (Shi et al., 23 Apr 2025).

6. Design Trade-offs, Limitations, and Future Directions

While modularization provides systematic advantages, several open challenges and trade-offs remain:

• Overhead and coordination: Modular architectures may introduce additional complexity in synchronization and interface management, and may incur overhead for cross-module communication or sequential phase execution (e.g., small increases in latency in MeVe). Abstractions must balance fine granularity against efficiency.
• Memory curation: Systems such as LEGOMem and memory-modular classification depend on the coverage and quality of memory units; curation pipelines and replacement policies become critical for effective generalization and avoidance of redundancy.
• Retrieval granularity and placement: Empirical ablations reveal that orchestration-level memories drive planning, whereas agent-specific memories impact execution details; optimal memory allocation remains context- and domain-specific (Han et al., 6 Oct 2025).
• Scalability and entropy: Management mechanisms (e.g., drift detection, contextual entropy) must trigger summarization or reflection to prevent fragmentation and incoherence in growing memory banks (Wedel, 28 May 2025).
• Cross-framework interoperability: Modularity enables transfer—e.g., evolved MemEvolve architectures generalize across LLMs and benchmarks, but robust, lossless transfer is not yet universal (Zhang et al., 21 Dec 2025).
• Real-world constraints: Hardware modular systems must address area, energy, and bandwidth scaling (as in LaMoS vs. ModSRAM), as well as the diversity of underlying physical modules and interconnects (Li et al., 5 Nov 2025, Asgari et al., 2018).
• Human-in-the-loop and governance: Contextual Memory Intelligence operationalizes audit, human annotation, and explainability in modular mechanisms for regulated domains such as healthcare (Wedel, 28 May 2025).

Research directions include continual memory updating, adaptive pruning, incorporation of multi-modal and hierarchical memories, better curation to maximize transfer, and deeper theoretical analyses of modular memory system limits and opportunities.

7. Synthesis and Outlook

Modular memory systems, by decomposing functionality into principled, independently tunable components, offer a systematic foundation for the design of scalable, adaptable, and auditable memory subsystems in both soft and hard computational agents, across scale and modality. Empirical results consistently validate efficiency gains, improved relevance, greater extensibility, and robustness to domain shift or task transfer. Continued research in abstraction layers, automated module evolution, compression and reflection mechanisms, and hardware–software co-design is poised to further advance the state of modular memory, enabling more reliable, performant, and general artificial agents and computing infrastructures (Ottem, 1 Sep 2025, Zhang et al., 21 Dec 2025, Zhang et al., 4 May 2025, Choi et al., 2023, Asgari et al., 2018, Li et al., 5 Nov 2025).