Modular Multi-Agent Systems

• Modular multi-agent systems are architectures that split memory tasks into independent modules, providing flexibility and clear auditability.
• They employ well-defined interfaces, plug-and-play extensibility, and pipeline or DAG configurations to streamline resource allocation and adaptation.
• Empirical studies reveal notable efficiency gains, such as reduced token counts and latencies, while improving robustness in multi-agent coordination and hardware integration.

A modular memory system is any architecture that decomposes the functions of storage, retrieval, updating, and management of memory into discrete, auditable, and independently reconfigurable modules. This design paradigm has been adopted across AI agents, LLMs, hardware accelerators, workflow systems, and quantum protocols to address scalability, controllability, robustness, and adaptability in memory-intensive applications. Modularity enables precise allocation of memory resources, differentiated policy tuning, and simple augmentation or replacement of any specific memory function without impacting the global system.

1. Architectural Principles of Modular Memory Systems

Modular memory systems are defined by explicit separation of functions into modules with well-specified interfaces, enabling independent development, testing, and optimization. Key architectural principles include:

• Well-Defined Modules and APIs: Core memory functions—typically encoding, storage, retrieval, and management—are implemented as distinct modules with explicit contracts (Zhang et al., 21 Dec 2025, Zhang et al., 4 May 2025).
• Pipeline or DAG Configuration: Modules are composed in a fixed sequence (strict pipeline) or as a directed acyclic graph, enforcing strict dataflow and observability guarantees. For example, in MeVe, five phases (Initial Retrieval, Relevance Verification, Fallback Retrieval, Context Prioritization, Token Budgeting) are orchestrated in a strictly pipelined fashion (Ottem, 1 Sep 2025).
• Plug-and-Play Extensibility: Any module (e.g., a semantic retriever or summarizer) can be replaced without modifying other modules, supporting easy adaptation to new tasks or hardware (Zhang et al., 4 May 2025, Zhang et al., 21 Dec 2025).
• Configuration and Auditability: Module choice, hyper-parameters, and prompts are centrally configured, supporting reproducibility and fine-grained tuning (Zhang et al., 4 May 2025).
• Data-Type Contracts: Inputs and outputs between modules are standardized (e.g., embeddings, memory items, scores, or context snapshots) (Wedel, 28 May 2025).

This modular philosophy contrasts with monolithic designs, where memory operations are tightly coupled and global logic must often be modified for each change.

2. Memory System Decomposition Across Domains

Modular memory has been instantiated in a range of domains. A representative selection includes:

System	Modules	Application Domain
MeVe	Retrieval, Verification, Fallback, Prioritization, Budgeting	LLM context construction (Ottem, 1 Sep 2025)
MemEvolve	Encode, Store, Retrieve, Manage	Self-evolving agent memory (Zhang et al., 21 Dec 2025)
LEGOMem	Full-task memory, Subtask memory	Multi-agent workflow automation (Han et al., 6 Oct 2025)
Memory Slices	DRAM, PMI, Systolic Compute, Network/Aggregation	AI hardware (Asgari et al., 2018)
HeTM/SHeTM	CPU TM, GPU TM, Merge, Validation	Heterogeneous trans. memory (Castro et al., 2019)
Contextual Memory Intelligence	Capture, Indexer, Drift Monitor, Regeneration, Reflection	Responsible AI (Wedel, 28 May 2025)
Livia	Emotion tagger, TBC, DIMF, Orchestration	AR companions (Xi et al., 12 Aug 2025)

Each system precisely delineates module boundaries (e.g., retrieval vs. verification in RAG; encode/store/retrieve/manage in agent memory), leading to significant gains in efficiency, performance, and clarity.

3. Mathematical Formulation of Modular Memory Operations

Modular memory modules are typically formalized as functions or operators acting on structured data. Common mathematical primitives include:

• Encoding: ℰ: ε → e, mapping experiences or data to structured embeddings or representations (Zhang et al., 21 Dec 2025).
• Storage: 𝒰: (M, e) → M′, updating persistent memory with a new encoded item (Zhang et al., 21 Dec 2025).
• Retrieval: ℛ: (M, q) → c, selecting relevant items from memory given a query, often as $$c_t = \arg\max_{x\in M_t}\;\mathrm{sim}\big(\phi(q),\,\psi(x)\big)$$ (Zhang et al., 21 Dec 2025, Zhang et al., 4 May 2025).
• Verification/Prioritization: Defining threshold-based filters (e.g., relevance score $s_i = V(q, c_i)$, $C_{ver} = \{c_i | s_i \geq \tau\}$) or redundancy suppression (cosine similarity redundancy checks) (Ottem, 1 Sep 2025).
• Reflection/Summarization: $\mathcal{G}(M)$, periodically consolidating or pruning memory entries based on defined policies (Zhang et al., 21 Dec 2025, Wedel, 28 May 2025).
• Budgeting/Pruning: Greedy packing ($C_{final}$) or dynamic importance thresholds (e.g., $S(m, t)$ compared to $\tau(t)$) (Ottem, 1 Sep 2025, Xi et al., 12 Aug 2025).

These formulations enable each module to be addressed as a black box with a clearly specified function, simplifying analysis, benchmarking, and replacement.

4. Representative Systems and Empirical Findings

4.1 LLM Context Construction

MeVe demonstrates the utility of modular memory for context control in LLMs. Its five-phase architecture enables drastic reductions in token context, with up to 75% savings (HotpotQA: 308.6→78.5 tokens, +0.18s latency), while ablation studies confirm the necessity of each phase (e.g., relevance verification cuts token count from 314 to 79.8) (Ottem, 1 Sep 2025).

4.2 Agent Memory Evolution

MemEvolve uses the (Encode, Store, Retrieve, Manage) schema and a meta-evolutionary algorithm, discovering adaptive, cost-efficient memory pipelines that consistently outperform fixed baselines (+17.06% pass@1 in agentic benchmarks) and transfer robustly across tasks and LLM backbones (Zhang et al., 21 Dec 2025).

4.3 Multi-Agent Workflow Automation

LEGOMem structures procedural memory as full-task and subtask units, assigned to orchestrators and agents, and shows that orchestrator memory is critical for planning, while agent memory improves execution. Vanilla LEGOMem boosts success by 12–13% across teams, outperforming competing designs such as Synapse and AWM, especially with smaller agents (Han et al., 6 Oct 2025).

4.4 Hardware and Quantum Systems

Memory Slices aggregate DRAM, programmable interfaces, systolic compute, and network modules per slice, scaling performance near-linearly and even superlinearly with slice count (e.g., S(256) ≈ 550× baseline for LSTM workloads) (Asgari et al., 2018). In quantum information, modular quantum memory modules with memory-enhanced fusion allow scalable multipartite entanglement at polynomial rather than exponential scaling in success probability, enabled by asynchronous buffering and fusion modules (Shi et al., 23 Apr 2025).

5. Performance, Scalability, and Engineering Trade-offs

• Token and Memory Efficiency: Modular filtering, prioritization, and budgeting enable up to 70% memory or context reduction without significant accuracy loss, as demonstrated in both LLMs (MeVe) and AR agents (Livia) (Ottem, 1 Sep 2025, Xi et al., 12 Aug 2025).
• Retrieval Latency and Parallelism: Modular vector-indexed memory models (e.g., MemEngine) scale up to large memory sizes, with retrieval latencies (e.g., ≈20 ms for 10k index) and near-linear or better speedup in parallel hardware instantiations (Zhang et al., 4 May 2025, Asgari et al., 2018).
• Adaptability and Robustness: The decoupling of memory storage from reasoning (as in memory-modular classification) allows new classes or domains to be inserted with no retraining, with performance matching or surpassing traditional models across zero-shot, few-shot, and incremental scenarios (Kang et al., 8 Apr 2025).
• Pluggability and Extensibility: Unified internal APIs (as in MemEngine, EvolveLab, SHeTM, and others) allow users to swap in different encoding, retrieval, or management modules, supporting rapid experimentation and benchmarking (Zhang et al., 4 May 2025, Zhang et al., 21 Dec 2025, Castro et al., 2019).

6. Limitations and Open Directions

• Dependence on Embedding Quality and Coverage: Retrieval and prioritization are only as effective as the underlying embedding models and coverage of stored experiences or trajectories (Han et al., 6 Oct 2025, Zhang et al., 4 May 2025).
• Auditability vs. Overhead: Strict modularity can induce overhead in pipeline complexity or cognitive load for maintainers, requiring careful balance between transparency and operational efficiency (Wedel, 28 May 2025).
• Robustness to Drift and Staleness: Addressing concept drift and irrelevant memory persistence requires continuous semantic drift monitoring and proactive management modules (e.g., as in CMI’s Insight Layer and Livia’s DIMF) (Wedel, 28 May 2025, Xi et al., 12 Aug 2025).
• Scalability Ceiling: While modular systems scale well in hardware and memory size, practical limits may still be imposed by index complexity, main-memory constraints, or network traffic in distributed settings (Asgari et al., 2018, Castro et al., 2019).
• Generalization across Modalities and Roles: Procedural/role-aware modular memory (as in LEGOMem) outperforms static exemplars, but semantic similarity-based retrieval may still conflate superficially similar but distinct memory units (Han et al., 6 Oct 2025).

7. Future Prospects and Research Trajectories

Several research trajectories are emerging:

• Bilevel and Meta-Optimization: Jointly evolving memory architectures and agent experiences in a modular framework (e.g., MemEvolve’s meta-evolutionary approach) for tailoring agent memory to environment demands (Zhang et al., 21 Dec 2025).
• Continual and Adaptive Updating: Incorporating continual learning, adaptive pruning, and summarization mechanisms to avoid staleness and support long-running deployments (Wedel, 28 May 2025, Han et al., 6 Oct 2025).
• Role-Aware and Multi-Agent Allocations: Allocating differentiated memories to roles or agents enhances coordination and execution, with modular assignment and retrieval essential for task decomposition in complex workflows (Han et al., 6 Oct 2025).
• Auditability, Traceability, and Human Oversight: Embedding explicit rationale capture, versioning, and user reflection (e.g., CMI’s Reflection Interface) enables longitudinal coherence, explainability, and compliance with governance regulations (Wedel, 28 May 2025).
• Hybrid Hardware and Quantum Architectures: Modular memory concepts are extending into compute-in-memory accelerators, quantum memory modules, and federated deployments for next-generation AI systems (Asgari et al., 2018, Chen et al., 18 Mar 2025, Shi et al., 23 Apr 2025).

A plausible implication is that, as modular memory systems continue to mature across software, hardware, and organizational boundaries, they will form the foundation for reliable, adaptive, and transparent AI, underlining memory as a dynamic infrastructure rather than a passive store.