Dual Memory Structures: Models & Applications

• Dual memory structures are systems combining fast, instance-based processing with slow, consolidated storage to balance plasticity and stability.
• They integrate methods like generative replay, reservoir sampling, and prioritized buffers to optimize learning and memory retention.
• Applications range from mitigating catastrophic forgetting in neural networks to enhancing performance in reinforcement learning and hardware designs.

A dual memory structure is a system integrating two distinct memory components—often with complementary characteristics—to provide enhanced stability, plasticity, computational efficiency, or representational capacity relative to single-memory designs. Dual memory architectures are central in diverse areas including computational neuroscience, continual learning, reinforcement learning, multi-agent planning, quantum information processing, memory hierarchy management, and hardware design. Instantiations range from fast/slow buffers in neural networks, main/cache buffers in reinforcement learning, to dual-rail or dual-mode physical memories in quantum and classical hardware.

1. Theoretical Foundations and Design Principles

The rationale for dual memory structures is grounded in the need to reconcile conflicting objectives—such as rapid adaptation (plasticity) with long-term stability (catastrophic forgetting avoidance)—and to efficiently capture task structure in environments with temporal, distributional, or contextual heterogeneity. The Complementary Learning Systems (CLS) theory, originally formulated to explain interaction between the mammalian hippocampus (fast, instance-based) and neocortex (slow, integrative), provides a direct computational analogy for many dual-memory frameworks, such as the Deep Generative Dual Memory Network (DGDMN) (Kamra et al., 2017) and Information-Theoretic Dual Memory Systems (ITDMS) (Wu et al., 13 Jan 2025). CLS-motivated dual memories are characterized by a short-term or fast memory (STM, working memory) for rapid assimilation of new events, and a long-term or slow memory (LTM, semantic/episodic memory) for consolidating and organizing knowledge.

Dual memory systems are also motivated by control-theoretic and computational considerations: decoupling training and storage (in RL), managing static constraints versus dynamic feedback (in multi-agent planning (Fan et al., 1 Nov 2025)), and bridging source-target dependencies in sequence modeling (dual associative memories (Weissenborn, 2016)). At the hardware level, dual-context architectures enable multiplexing of immutable and mutable data within the same physical substrate (Kaiser et al., 2023, Sheshadri et al., 2021).

2. Architectures and Formal Models

Dual memory structures are instantiated in myriad forms, with architectural choices reflecting specific domain requirements.

2.1 Fast/Slow Buffers and CLS Emulation

• DGDMN (Kamra et al., 2017): Fast short-term task memories (STTMs) with high plasticity operate per-task and are periodically consolidated into a slow, generative LTM via generative replay.
• ITDMS (Wu et al., 13 Jan 2025): Implements a fast buffer updated by reservoir sampling for uniform coverage of recent data, and a slow buffer optimized by information-theoretic diversity and class balance, with explicit sample selection via Rényi entropy and Cauchy–Schwarz divergence.
• DUCA (Gowda et al., 2023): Fast working memory ($N_{WM}$) rapidly fits new data; semantic memory ($N_{SM}$) consolidates knowledge via stochastic exponential moving average from $N_{WM}$, with loss coupling enforced through knowledge-sharing and regularization terms.

2.2 Main/Cache Stratification in RL

• Dual Memory Structure for DQN (Ko et al., 2019): Replay buffer is split into main memory (MM) for archival transitions and cache memory (CM) for prioritized, high-frequency training updates. CM supports both prioritized sampling (PER) and prioritized eviction (PSMM), confining compute focus to a tractable subset.

2.3 Dual-Bank and Dual-Mode Physical Memories

• CS/DC 8T SRAM (Kaiser et al., 2023): Dual-context ROM-augmented RAM overlays static ROM (via multi-$V_T$) and dynamic RAM in the same cell, with context-switching (RAM-only/ROM-only) and dual-context (simultaneous) access.
• 8T/7T Augmented SRAM (Sheshadri et al., 2021): 8T cell provides static SRAM-like and dynamic DRAM-like storage per cell; 7T cell supports ternary dynamic storage. Duality arises from configuration of access paths and retention times.
• Dual-mode Optical Cavity Memory (Hanamura et al., 13 Feb 2025): Quantum memory supports both storage (memory mode) and mode-mixing (beam-splitter/entanglement mode) by dynamic control of out-coupling, allowing multiplexed quantum operations.

2.4 Cognitive and Symbolic Dual Memories

• EvoMem (Fan et al., 1 Nov 2025): Constraint Memory (CMem) encodes stable, query-level constraints; Query-feedback Memory (QMem) accumulates feedback for in-situ error correction.
• ExpeRepair (Mu et al., 12 Jun 2025): Episodic memory stores concrete repair demonstrations (vector-keyed), while semantic memory retains abstract insights in natural language; both are jointly activated during LLM-based program repair to inform dynamic prompt construction.

3. Algorithms, Update Rules, and Information Flow

The dual memory paradigm is closely coupled to memory update and retrieval mechanisms, which are pivotal for balancing recency and retention, or static and dynamic information.

• Selective Buffer Updates: Fast buffers (e.g., ITDMS, DGDMN) are updated with constant-time operations (reservoir sampling, task-based replay), while slow buffers/long-term stores are selectively refreshed using diversity- and balance-driven criteria or via generative replay.
• Memory Consolidation: In DGDMN, periodic 'sleep' consolidates STTM knowledge into LTM using generated pseudo-samples; DUCA's semantic memory assimilates working memory through exponential moving averages.
• Sampling and Prioritization: In DMS-RL, cache memory is replenished by stratified sampling from main memory and recent transitions; eviction is stochastic but biased by transition importance (TD-error).
• Attention and Content-Addressability: In dual associative memory RNNs (Weissenborn, 2016), and dual-memory anomaly detectors (Guo et al., 2021), content-based (often key-value or similarity-based) retrieval mediates write/read operations, enabling efficient lookup and credit assignment.
• Dual-Context Readout: In hardware, dual-context SRAM arrays employ two-phase sensing (RAM-first, then ROM) to extract both bits without restoring the cell, requiring signal/threshold engineering.

4. Representative Applications Across Domains

Dual memory structures are now integral to a range of tasks:

• Continual Learning: Dual memory designs are employed to mitigate catastrophic forgetting and enable long-term knowledge retention in neural networks (Kamra et al., 2017, Gowda et al., 2023, Wu et al., 13 Jan 2025). Fast/slow separation supports both plasticity (learning new tasks) and stability (retaining past knowledge).
• Reinforcement Learning: Dual buffer architectures offer improved sample efficiency, computational scalability, and final performance relative to single-buffer storage, as evidenced by >2× test score improvements in Atari games (Ko et al., 2019).
• Multi-Agent and Planning Systems: Separating constraint memory and dynamic feedback memory enables agents to robustly coordinate, track constraints, and iteratively refine solutions in structured planning tasks (Fan et al., 1 Nov 2025). Empirically, this yields significant (>10–17%) absolute accuracy increases on planning benchmarks.
• Symbolic AI and LLM-driven Systems: Retrieval-augmented prompting (episodic + semantic memory) supports improved generalization and functional repair of software repositories (Mu et al., 12 Jun 2025), mapping directly to human episodic-semantic retrieval.
• Hardware and Quantum Information: Dual-context physical memory enables AI/ML accelerators, IoT systems, and quantum optical processors to interleave immutable and mutable data storage at the same physical site (Kaiser et al., 2023, Sheshadri et al., 2021, Hanamura et al., 13 Feb 2025).
• Perception and Tracking: Dual memories capturing target/background prototypes enhance robustness to occlusion, distractors, and dynamic context in low-latency video tracking (Shi et al., 2019).

5. Empirical Performance and Quantitative Outcomes

Systematic evaluation of dual memory structures consistently demonstrates significant increases in retention, sample efficiency, or phase fidelity over monolithic or naive baselines.

Domain / Task	Dual-memory design	Improvement (vs. single memory)	Reference
Continual learning (Split-CIFAR, TinyImageNet)	Fast/slow buffer (ITDMS, DGDMN, DUCA)	+2–5% absolute; >4× less forgetting	(Wu et al., 13 Jan 2025, Kamra et al., 2017, Gowda et al., 2023)
RL (Atari DQN)	Main + cache, prioritized (DMS)	>2× test score	(Ko et al., 2019)
Program repair (LLM)	Episodic + semantic retrieval	+6.4% pass@1 w/ both vs. no-memory	(Mu et al., 12 Jun 2025)
Planning (multi-agent)	Constraint + feedback memory	+9–17% planning accuracy	(Fan et al., 1 Nov 2025)
Video anomaly detection	Normality/abnormality dual memories (DREAM)	+3–4% AUC; robust at low anomaly rates	(Guo et al., 2021)
Hardware (ROM-augmented RAM)	Dual-context 8T SRAM	~1.3x area improvement; BER <10^{-9} in dual-mode	(Kaiser et al., 2023)
Quantum memory	Dual-mode optical cavity	93% storage efficiency; TDM scalability	(Hanamura et al., 13 Feb 2025)

6. Limitations, Open Problems, and Improvements

Despite their demonstrated advantages, dual memory structures introduce domain-specific challenges:

• Capacity and Refresh: In hardware designs, added complexity (e.g., retention time, sense thresholds in dynamic/dual bits) imposes refresh and integration tradeoffs (Kaiser et al., 2023, Sheshadri et al., 2021).
• Latency and Scalability: Real-world workloads with adversarial or streaming access patterns can challenge hit/miss ratio scalability in dual-level paging approaches (Oren, 2017); slow memory access may offset hit gains.
• Optimization and Regularization: Dual memory neural architectures (AM-RNNs, DGDMN) may require additional regularization or supervision to enforce key diversity, sparseness, and to prevent degenerate behaviors (Weissenborn, 2016).
• Initialization and Synchronization: For deterministic tree exploration, linear time is only achievable in dual-memory models with clean node initialization or synchronizing tokens; absence of both may lead to $\Omega(n^2)$ time (Bojko et al., 2021).

Potential improvements include adaptive thresholds, dynamic weighting in paging, advanced diversity or sparsity constraints, and more physiologically motivated consolidation policies.

7. Cognitive, Algorithmic, and Physical Generalization

The dual memory concept is broadly applicable and continues to inform innovation across computational neuroscience, cognitive architectures, and systems design. Human memory theory provides a persistent inspiration: division into episodic vs. semantic, working vs. long-term, or phonological vs. scratchpad buffers is realized in machine systems as concrete data structures, attention mechanisms, and control flows. Dual-memory approaches serve as a principled method for optimizing for stability-plasticity dilemmæ, scalable retention, cross-modal integration, and resource-efficient memory access in high-complexity, evolving tasks.

Future work is expected to further integrate dual-memory structures with advanced consolidation routines, resource-aware hardware, quantum platforms, and retrieval-augmented generative reasoning, while addressing efficiency and robustness at scale across domains (Kamra et al., 2017, Sheshadri et al., 2021, Hanamura et al., 13 Feb 2025).