Layered Memory Systems

• Layered memory systems are architectures that decompose memory into discrete, specialized layers for rapid recall, long-term storage, and dynamic control.
• They integrate mechanisms for encoding, retrieval, decay, and promotion to enhance efficiency and adaptability across cognitive and hardware domains.
• Implementations like COLMA, HAM, and MemOS demonstrate scalable performance gains, improved learning stability, and energy-optimized hardware designs.

Layered memory systems are computational architectures that organize memory into a hierarchy of discrete, interacting strata, each designed with specialized mechanisms for encoding, retrieval, persistence, and governance. These systems span applications in deep learning, neuromorphic computing, high-performance hardware, and cognitive-inspired AI, offering improved efficiency, interpretability, and adaptability by explicitly separating memory along timescales, functions, or physical realization. Their design contrasts with monolithic or “flat” architectures, providing enhanced scalability, dynamic control, and better alignment with both biological cognition and technological constraints.

1. Conceptual Foundations of Layered Memory

Layered memory systems are grounded in the hypothesis that memory’s functional requirements—such as rapid recall, long-term storage, reasoning, and lifelong learning—are best served by decomposing storage and access across multiple, distinct levels, each with tailored capacity, persistence, and addressing mechanisms.

Several frameworks exemplify this paradigm:

• Cognitive Layered Memory (COLMA) organizes memory into five layers (Physical Persistence, Knowledge Category, Coordination, Functionality, and User Scenario), explicitly mirroring sensory, working, episodic, semantic, and long-term memory in humans. Each layer implements its own encoding, retrieval, and consolidation operators, and the data flow is mediated by attention-like routing and multimodal fusion mechanisms (Cai et al., 16 Sep 2025).
• Hierarchical Associative Memory (HAM) generalizes modern Hopfield networks to recurrent, arbitrarily deep architectures where lower layers encode primitives and higher layers perform pattern assembly, coupled via energy-based attractor dynamics with explicit Lyapunov convergence guarantees (Krotov, 2021).
• Layered Memory in LLMs encompasses architectures where parametric, activation-based, and external (non-parametric) memories form a strict hierarchy, coordinated by operating systems such as MemOS, which introduce units like MemCube for unified access, migration, and versioning (Li et al., 4 Jul 2025, Zhang et al., 23 Sep 2025).
• Hardware Layered Memory includes physical hierarchies in 3D-stacked DRAM (e.g., Simultaneous Multi Layer Access, SMLA (Lee et al., 2015)), tiered monolithic DRAM (Stratum (Pan et al., 6 Oct 2025)), and multi-tier resistive memory systems (Park et al., 2023).

Key unifying properties are: discrete layers with distinct time constants and data structures, bidirectional inter-layer signaling (including bottom-up and top-down modulation), and governance for migrating, fusing, or pruning traces.

2. Formal Models and Mechanisms

Memory operations in layered systems are rigorously defined at each level:

• Encoding & State Evolution: For each layer $i$, the state $M_i^t$ is updated via $M_i^{t+1} = M_i^t \cup \{E_i(s)\}$ where $E_i(\cdot)$ encodes the input stimulus $s$. Layers may implement composite update rules incorporating consolidation, decay, and fusion with retrieved items.
• Retrieval: Each layer exposes a retrieval operator $R_i(q; M_i)$, typically realized as an attention, top-$k$ key-value lookup, or similarity-matching function, often over multimodal representations (Cai et al., 16 Sep 2025, Berges et al., 12 Dec 2024).
• Decay, Pruning, and Promotion: Many systems introduce explicit recency decay, usage-weighted pruning (thresholding on $U_i(m) = \text{freq}_i(m)\cdot\text{imp}_i(m)$), and promotion/demotion policies, as in TradingGPT’s composite ranking score $\gamma_i^E$ or MemOS’s value-driven tiering (Li et al., 2023, Li et al., 4 Jul 2025).
• Bidirectional Signaling: In models such as HAM (Krotov, 2021) and experience-driven visual memory (0905.2125), top-down feedback is formally coupled via tied synaptic weights and energy functions, ensuring convergence to joint attractors composed of primitives and composite assemblies.
• Layered Latent State Support: LLM-focused architectures utilize rolling, per-layer buffers of internal state (“activation-based memory”) and implement updates as contextual reweaving or staged attention over buffer traces, retaining token or event trajectories throughout model depth (Dillon et al., 4 Feb 2025).

3. Practical Implementations Across Domains

Layered memory systems span varied substrate domains, each with concrete design and deployment details:

Application Domain	Layer Examples	Access/Update Mechanisms
Cognitive AI / AGI Architectures (Cai et al., 16 Sep 2025)	USL, FL, CL, KCL, PPL (sensory, short-term, working, semantic, persistent)	Per-layer encoding, attention-based retrieval, dynamic fusion, adaptive pruning
Deep Learning Models (Berges et al., 12 Dec 2024, Zhang et al., 23 Sep 2025)	Parametric, activation (contextual), external, episodic/procedural	Gradient-driven write, prompt concatenation, retrieval-augmented generation
Reinforcement/Trading Agents (Li et al., 2023)	Short, middle, long-term (working, episodic, semantic)	Recency/relevance scoring, thresholded retention, inter-agent debate
Memory Hierarchical Hardware (0710.4656, Seshadri, 2016, Lee et al., 2015, Pan et al., 6 Oct 2025)	Cache, DRAM, off-chip layers; SMLA/Stratum tiers; RRAM stacks	Prefetch, migration, in-DRAM copy/compute, tiered mapping/placement
Photonic & Neuromorphic Systems (Park et al., 2023, Li et al., 13 Feb 2025)	Trilayer RRAM (barrier/dense/porous oxide), Bi-layer In2Se3	Bulk switching, analog modulation, pulse-induced phase transitions

• In transformer architectures, memory layers are often realized as trainable key-value tables situated between attention and feed-forward blocks. Queries select a sparse subset of keys and retrieve corresponding values, with parameter scale up to 128B in production LLMs (Berges et al., 12 Dec 2024).
• MemOS unifies three memory types as schedulable resources in an OS-like architecture. Management routines such as MemScheduler promote/demote MemCubes between external, activation-cache, and parameter memory, according to usage and policy (Li et al., 4 Jul 2025).
• In hardware, SMLA and Stratum exploit the vertical structure of DRAM to multiplex internal layer bandwidth, assign tiers to hot and cold data, and pair near-bank compute with fine-grained placement for dramatic latency and throughput improvements (Lee et al., 2015, Pan et al., 6 Oct 2025).

4. Performance, Capacity, and Adaptivity

Layered designs enable:

• Improved compute–capacity ratio: Memory layers achieve parameter-to-FLOP ratios orders of magnitude higher than dense layers at comparable or lower inference cost. For example, in “Memory Layers at Scale,” N=1M, k=4 yields a ~250k× capacity per FLOP compared to dense FFNs (Berges et al., 12 Dec 2024).
• Dynamic adaptability: Scheduling and migration policies (e.g., value-driven tiering in MemOS, recency scoring in TradingGPT) allow systems to seamlessly adapt to changing access patterns, hot/cold data, and workload shifts, optimizing for latency and storage efficiency (Li et al., 4 Jul 2025, Li et al., 2023).
• Resistance to interference and catastrophic forgetting: Hierarchical self-organization with plasticity and homeostasis maintains stable attractors under continuous learning, a property critical for lifelong and edge applications (0905.2125, Cai et al., 16 Sep 2025).
• Multi-timescale support: By assigning distinct memory tasks to layers of varying persistence (milliseconds to years), systems can optimize for both recency-sensitive recall and long-term retention (Cai et al., 16 Sep 2025, Li et al., 2023).

For hardware, layered access schemes achieve up to 60% execution time and 70% energy reductions in memory-intensive workloads (MHLA+TE) (0710.4656); 3D DRAM SMLA increases bandwidth 4X for a 4-layer stack while saving up to 18% DRAM energy (Lee et al., 2015); Stratum’s tiered Mono3D DRAM delivers $8.29\times$ decoding throughput and $7.66\times$ energy efficiency gain over GPU baselines (Pan et al., 6 Oct 2025).

5. Evaluation Protocols, Governance, and Safety

Layered memory systems necessitate comprehensive, regime-aware evaluation and management:

• Layered Evaluation Protocols: E.g., a three-regime approach (parametric-only, offline retrieval, online retrieval) ensures that information availability is decoupled from LLM capability, with layered metrics for recall, faithfulness, grounding, and procedural consistency (Zhang et al., 23 Sep 2025).
• Governance and Dynamic Management: Dynamic Memory Management (DMM Gov) coordinates update/forget/rollback via pre-registered thresholds and audit protocols, achieving safety, updatability, and rollback capabilities crucial for real-world deployment (e.g., health care, code completion) (Zhang et al., 23 Sep 2025).
• Testable Propositions: Theoretical guarantees on identifiability, minimal evaluation sufficiency, verifiable forgetting, and comparative advantage of RAG/small-window over long-context reading provide a reproducible coordinate system for future research (Zhang et al., 23 Sep 2025).
• Auditability and Lifecycle: Metadata management via constructs like MemCube supports controlled promotion, migration, and versioning, ensuring traceability and compliance (Li et al., 4 Jul 2025).

6. Extensions and Future Directions

Current limitations and anticipated advances include:

• Integration with neuromorphic, photonic, and spintronic substrates: Layered crossbar arrays (trilayer RRAM, photonic In₂Se₃ phase-change) deliver high-density, low-power, analog-accessible memory, tailored for real-time edge AI and embedded learning (Park et al., 2023, Li et al., 13 Feb 2025).
• Hierarchical compositionality and reasoning: Further development of energy-based, attractor-layered networks is expected to enhance pattern completion and denoising tasks, possibly integrating with transformers and attention mechanisms (Krotov, 2021, Dillon et al., 4 Feb 2025).
• Advanced hardware–software co-design: Scaling tiered DRAM to 1000+ layers (Mono3D), integrating fine-pitch vertical interconnects and programmable, workload-driven tiering tables, will further align physical hierarchy with algorithmic data movement and inference needs (Pan et al., 6 Oct 2025).
• Enhanced multimodal and lifelong memory: Layered designs such as COLMA and procedural memory in LLMs aim to unify symbolic, vector, and temporal traces, supporting continuous adaptation, robust forgetting, and multimodal fusion suitable for AGI-scale systems (Cai et al., 16 Sep 2025, Zhang et al., 23 Sep 2025).

7. Comparative Analysis and Benchmarking

Layered architectures demonstrably outperform flat or monolithic designs in adaptability, explainability, and empirical task performance:

System	Multimodal Integration	Dynamic Update	Catastrophic Forgetting	Interpretability	Scalability
Flat RAG	✗	✗	prone	limited	limited
COLMA	★★★	★★★	resilient	traceable	distributed
MemOS	unified	versioned	auditable	full metadata	modular
Memory Layer (LM)	strong (for factual, coding)	on-the-fly	robust (when sparse)	latent traceable	to 128B

Empirical benchmarks show memory-augmented transformers with layered memory can match or exceed models with 2–4× more compute or parameter scale on factual QA, code generation, and reasoning tasks, with stable energy and latency profiles (Berges et al., 12 Dec 2024, Dillon et al., 4 Feb 2025, Cai et al., 16 Sep 2025, Li et al., 2023).

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In sum, layered memory systems furnish a unifying architectural and operational principle bridging neuroscience, deep learning, neuromorphic engineering, and physical hardware. By decomposing memory into explicit, dynamically governed layers, these systems deliver measurable gains in compute efficiency, learning stability, multimodal integration, and dynamic adaptability—foundational properties for next-generation AI and memory-centric computing (Cai et al., 16 Sep 2025, Li et al., 2023, Berges et al., 12 Dec 2024, Li et al., 4 Jul 2025, Pan et al., 6 Oct 2025, 0710.4656, Zhang et al., 23 Sep 2025).