Memory-Based Continual Retrieval

Updated 7 January 2026

Memory-based continual retrieval is a machine learning paradigm that uses explicit memory systems to continuously store and update evolving data.
It employs diverse retrieval algorithms such as similarity search, token-level interaction, and cross-attention to efficiently match queries with stored representations.
The approach mitigates catastrophic forgetting through dynamic memory updates, experience replay, and prototype-guided consolidation.

Memory-based continual retrieval is a class of machine learning methodologies that leverages external or explicit memory structures to sustain data retrieval capabilities as new information streams in, enabling continual learning and dynamic adaptation while mitigating catastrophic forgetting. This paradigm contrasts with solely parametric models, combining retrieval, storage, and consolidation mechanisms designed for tasks where queries, knowledge, or classes evolve over time.

1. Core Principles and Memory Architectures

Memory-based continual retrieval frameworks allocate and organize external memory modules which persist task-relevant information beyond the underlying model's fixed parameters.

Explicit Memory Units: Architectures such as analog or digital crossbar arrays (e.g., phase-change memory) store class prototypes or support vectors in a dynamically expanding memory layout, allowing incremental storage and in-situ modification without reinitialization (Karunaratne et al., 2022).
Banked and Compressed Memories: Compressors or embedding functions encode documents or multimodal experiences into compact, often learnable, vectors or sub-symbolic representations. Memory banks or structured graphs (e.g., hierarchical knowledge graphs) collect and index these summaries, supporting efficient query-time retrieval and similarity search (Li et al., 2024, Liu et al., 3 Dec 2025).
Gradient-Based Buffers: Experience replay buffers maintain a small, representative sample of past data, whose selection can be optimized for maximal information preservation (e.g., by gradient interference criteria) (Aljundi et al., 2019).
Associative and Hopfield Networks: Fragmented or highly sparse memories are stored as attractor patterns in Hopfield (or modern Hopfield) networks, enabling content-based, partial-cue completion and robust reconstruction from fragmentary states (Bai et al., 2023).

The explicit design and operational logic of the memory (e.g., prototype-based, cluster-based, graph-based, or Hopfield-based) determine the mechanism by which continual adaptation and retrieval are realized.

2. Retrieval Algorithms and Similarity Mechanisms

Efficient and robust retrieval is central to the continual use of memory. Multiple strategies are employed:

Similarity Search on Structured Memory: Queries are encoded using the same or analogous embedding mechanism, and retrieval is performed by computing dot-product, cosine similarity, or maximum activation across stored memory units (e.g., analog dot-product in PCM crossbar arrays (Karunaratne et al., 2022) or cosine over knowledge graph nodes (Liu et al., 3 Dec 2025)).
Fine-Grained Token-Level Interaction: Token-wise representations with late-interaction similarity metrics (such as ColBERT-style maximum-similarity summing over tokens) enable semantically precise matching, crucial in streaming text retrieval (Son et al., 6 Jan 2026).
Cross-Attention Over Condensed Memory Slots: Aggregating top-k most relevant compressed memory vectors using cross-attention and aggregation networks enables scalable relevance scoring in large document banks (Li et al., 2024).
Parzen Window and Kernel-Based Retrieval: For policy or action retrieval in RL-based models, similarity kernels (e.g., Gaussian Parzen windows) weight the contributions of stored episodic experiences, dynamically interpolating prior and memory-based behavior (Wang, 27 Dec 2025).

Such mechanisms must balance retrieval fidelity, computational efficiency, and continual scalability, especially as the memory size increases or task diversity expands.

3. Memory Update, Compression, and Consolidation

Continual learning systems must devise memory update protocols to accommodate new data while managing finite capacity and avoiding overwriting key information.

Dynamic Expansion and In-Situ Superposition: Memory units expand incrementally for new classes or domains. In hardware (PCM arrays), new class columns are allocated and physical superposition of support examples is accomplished by partial SET pulses to memory cells, enabling aggregation without erasure (Karunaratne et al., 2022).
Cluster Prototyping and Soft Assignment: Adaptive cluster-based memories maintain soft or regularized prototypes. New queries or documents are mapped into clusters via similarity thresholds, with cluster formation, assignment, and decay rules to regularize size and fidelity (Son et al., 6 Jan 2026).
Compression via Learnable Soft Tokens: New documents are mapped to a fixed number of (learnable) memory tokens using cross-attention transformers, with optional self-matching losses to specialize representations (Li et al., 2024).
Prototype-Guided Selection: Representative exemplars are systematically maintained by storing only examples closest in embedding space to current class prototypes, with periodic buffer refresh to ensure representativity under shifting class boundaries (Ho et al., 2021).
Distillation and Semantic Consolidation: For scalable memory growth, periodic distillation compresses frequently accessed facts from external memory into the parametric model, while knowledge graphs are pruned and merged by frequency and semantic similarity (Liu et al., 3 Dec 2025).

Table: Memory Update Strategies in Representative Systems

System	Update Mechanism	Memory Structure
PCM Crossbar (Karunaratne et al., 2022)	Partial-SET superposition	Analog columns (classes)
CMT (Li et al., 2024)	Learnable compressor, top-k agg.	Token memories
CREAM (Son et al., 6 Jan 2026)	RP-LSH clusters, coreset samp.	Clustered embeddings
Prototype-replay (Ho et al., 2021)	Nearest-to-prototype update	Fixed per-class buffer
MemVerse (Liu et al., 3 Dec 2025)	Graph consolidation, distill.	Multimodal KG + STM/LTM

4. Catastrophic Forgetting Mitigation

The core objective of memory-based continual retrieval is to address catastrophic forgetting, the phenomenon where neural networks forget previously learned information upon training on new data.

Experience and Generative Replay: Selective storage of past samples or pseudo-samples in a buffer, with priority given to those whose loss would increase most under new gradient steps (maximally interfered retrieval), directly counters forgetting by focusing review on "at-risk" examples (Aljundi et al., 2019).
Parameter Freezing Strategies: Many frameworks freeze or shield the main model’s parameters—instead, learning and adaptation proceed solely through memory update or controller fine-tuning, avoiding parameter drift (Li et al., 2024, Wang, 27 Dec 2025).
Knowledge Distillation with Dynamic Weighting: Regularizers (e.g., dynamic Fisher, contrastive knowledge transfer) enforce preservation of key alignment or decision boundaries from previous stages, balancing plasticity for new data with stability for older knowledge (Chen et al., 15 Dec 2025).
Associative and Attractor Networks: Memory fragments are encoded into associative structures supporting fast, convergent recall, significantly lowering forgetting rates compared to generative-autoencoder-style reconstruction or random experience replay (Bai et al., 2023).

These mechanisms are evaluated using metrics such as backward transfer (BWT), average accuracy, and retention on previously learned domains.

5. Scalability, Efficiency, and Practical Considerations

Memory-based continual retrieval designs address scaling challenges through compression, pruning, and hardware-aware algorithms.

Energy and Latency Savings: Crossbar-based hardware implementations achieve in-memory retrieval at sub-microsecond latencies and low energy budgets, outperforming software baselines in both efficiency and accuracy (Karunaratne et al., 2022).
Compression/Distillation: Systems like CMT and MemVerse compress memories into minimal key-value representations or distilled parametric sub-models to keep storage and compute tractable as task diversity grows (Li et al., 2024, Liu et al., 3 Dec 2025).
Cluster Management and Pruning: Soft clustering with assignment and decay rules bounds memory growth, while stratified sampling ensures representative pseudo-labeling for continual self-supervision (Son et al., 6 Jan 2026).
Online, Label-Free Adaptation: Unsupervised adaptation, as in CREAM, avoids the need for ground-truth query-label pairs, instead relying on memory-driven pseudo-labels and triplet losses to maintain retrieval quality under distribution shift.

6. Applications and Empirical Benchmarks

Memory-based continual retrieval techniques have been evaluated across modalities (vision, language, multimodal biomedical), tasks (few-shot classification, IR, QA, continual reinforcement learning), and deployment targets (hardware, LLMs, agents).

Few-Shot and Class-Incremental Learning: PCM-based explicit memory achieves ≤2.5% accuracy drop versus full-precision baselines when expanding from 60 to 100 classes on CIFAR-100 and miniImageNet, outperforming software-only and prior hardware baselines (Karunaratne et al., 2022).
LLM Adaptation: CMT offers up to +4.07 EM and +4.19 F₁ gains over prior retrieval-augmented LLM baselines on question answering under streaming document updates (Li et al., 2024).
Information Retrieval: CREAM provides +27.79% Success@5 and +44.5% Recall@10 advantages, exceeding both BM25 and dense retrieval continual learners—demonstrating the effectiveness of hybrid cluster- and token-based memory (Son et al., 6 Jan 2026).
Multimodal and Domain-Generalist Retrieval: Multi-modal, retrieval-augmented models like PRIMED attain SOTA transfer and backward-transfer in cross-modality, high-class-count medical tasks (ACC=68.6, BWT=–2.7 on MedXtreme) (Chen et al., 15 Dec 2025).
Associative Fragmentary Memory: SHARC’s selective, content-based replay improves task IL and CL accuracy by 3–7% over random or uniform experience replay while substantially reducing replay buffer size (Bai et al., 2023).

7. Limitations and Open Research Questions

Current systems exhibit several practical and theoretical trade-offs:

Computational Overhead: Fine-grained retrieval, frequent memory updates, and clustering can introduce significant overhead, especially for high-throughput or multimodal streams (Son et al., 6 Jan 2026).
Memory Management: Deciding memory allocation, placement, and retention thresholds (e.g., prototype count per class, cluster pruning radii) requires careful domain-specific tuning to avoid redundancy or loss of coverage (Ho et al., 2021, Son et al., 6 Jan 2026).
Noisy Pseudo-Labeling: Memory-driven or self-supervised triplet sampling can introduce noise, especially with overlapping or ambiguous clusters in evolving corpora, impacting downstream retrieval fidelity (Son et al., 6 Jan 2026).
Order Sensitivity: Certain prototype or buffer-based methods remain sensitive to task or domain order, with observed variance across curriculum sequences (Ho et al., 2021).
Integration with Parametric Models: Effective strategies for dynamic distillation, hybrid RAG, and balancing hard/soft (explicit/implicit) memory under deployment constraints remain active areas (Liu et al., 3 Dec 2025, Li et al., 2024, Zhang et al., 2 Dec 2025).