Adaptive Key-Value Memory Banks

• Adaptive key-value memory banks are dynamic memory architectures that adjust storage based on semantic importance and computational constraints, enhancing neural system performance.
• They employ advanced insertion, eviction, and compression techniques—such as three-factor plasticity, LFU pruning, and hierarchical attention—to boost retrieval speed and memory efficiency.
• Applications span long-context LLM inference, biologically plausible associative memory, and multimodal systems, underscoring their role in scalable neural and cognitive architectures.

Adaptive key-value memory banks are a class of architectures and algorithms designed to optimize the storage, retrieval, and compression dynamics of external key-value stores, particularly in neural and neuromorphic systems. These systems appear in diverse domains—transformer-based LLMs, biologically plausible associative memory, high-performance distributed storage, vision models, and explicit cognitive tape architectures. Central to the adaptive paradigm is the flexibility to adjust the size, organization, eviction policy, redundancy, and update regime of key-value pairs, often according to utilization, semantic importance, modality, or computational constraints.

1. Adaptive Key-Value Memory Architectures

Key-value memory banks instantiate a mapping between an addressable key (vector/embedding) and a value (often a label, token, or high-dimensional feature). Architecturally, implementations vary:

• Slot-based banks (Tyulmankov et al., 2021): Input $\to$ Hidden (slot) $\to$ Output, with $K\in\mathbb{R}^{N\times d}$ for keys, $V\in\mathbb{R}^{m\times N}$ for values. Each slot represents a discrete memory location, overwritten in one-shot with new pairs under controlled plasticity.
• External million-scale explicit stores (Yu et al., 3 Nov 2025): A tensor $\mathcal{M}\in\mathbb{Z}^{N\times L}$ for values (natural-language token sequences), with learnable key embeddings $k_i\in\mathbb{R}^d$.
• Distributed redundancy banks (Kleyko et al., 2022): Memory shape $[r \times d]$ keys and $[r \times m]$ values, where $r$ is a free parameter controlling intra-bank redundancy.
• Adaptive memory management in video segmentation (Pourganjalikhan et al., 2022): Per-frame keys and values of shape $[H'W'\times C^k]$/$[H'W'\times C^v]$, managed as a dynamic bank of frame features.

This diversity allows banks to support fast content-addressable retrieval, biologically plausible updates, scalable QA reasoning, and resource-efficient context management.

2. Adaptive Insertion, Eviction, and Compression Mechanisms

Adaptive insertion, retention, and compression policies are foundational. Strategies include:

• Three-factor plasticity and meta-learned gating (Tyulmankov et al., 2021): Writes and erases are gated by global novelty and local eligibility signals $(q_t,\gamma_{t,i})$, yielding rapid overwrite with biological plausibility.
• Heavy-hitter and window-based selection (Wu et al., 18 Dec 2024, Feng et al., 16 Jul 2024): Decoupled strategies for prefill (global “attention-sink” windows) vs. decoding phases (sliding windows, Top-K by attention), adaptive and discontinuous updates, and layer/head-wise allocation.
• LFU-indexed pruning (Pourganjalikhan et al., 2022): Each slot maintains $(n_i, a_i)$ (reference count, age), forming an importance score $S_i = n_i / a_i$; least-useful entries are evicted under bounded capacity $N_{\max}$.
• Segment-aware and block-adaptive eviction (Chen et al., 26 Oct 2025): Linguistic segmentation guides boundaries, and per-segment block sizes are chosen so semantic coherence is maximized under budget $B$; attention-diversity boosts are applied.
• Modality- and head-adaptive selection (Li et al., 6 Jun 2025, Feng et al., 16 Jul 2024): Tokens are ranked by proxy-attention, partitioned by text/visual origins, and budgets are dynamically allocated per attention head and modality.

Compression is achieved via redundancy reduction (Kleyko et al., 2022), quantization (adapters and residuals) (Shutova et al., 31 Jan 2025), and selective quantized eviction (Chen et al., 26 Oct 2025, Feng et al., 16 Jul 2024).

3. Retrieval and Readout Operations

Retrieval mechanisms leverage bank organization for efficient, differentiable access:

• Softmax similarity interpolation (Tyulmankov et al., 2021): Given query $\tilde{x}$, compute $h=\mathrm{softmax}(K\tilde{x})$, output $\tilde{y}=Vh$.
• Two-stage filtering (Yu et al., 3 Nov 2025): Coarse filtering via product key decomposition reduces candidates from $\mathcal{O}(N|I|)$ to $\mathcal{O}(\sqrt{N}|I|)$; fine-grained Gumbel-Softmax enables end-to-end differentiable selection over candidates.
• Distributed class superposition (Kleyko et al., 2022): Distributed key memory formed by superposed codeword–outer-product terms enables direct projection of the query to class scores without explicit index lookup.
• Hierarchical attention (Li et al., 2018): Multi-bank structures support hierarchical scrutiny: within-bank attention softmax, followed by bank-level weighting for answer assembly.

Efficient Top-K selection—by attention, recency, or proxy tokens—underpins most compression strategies for LLMs (Wu et al., 18 Dec 2024, Gu et al., 4 Jun 2025, Li et al., 6 Jun 2025, Chen et al., 26 Oct 2025).

4. Learning and Update Procedures

Adaptive banks incorporate both non-learned gating rules and meta-learned optimization:

• Gradient-based meta-learning of update rules (Tyulmankov et al., 2021): Frameworks with parametric plasticity kernels converge to biologically plausible three-factor rules (pre-synaptic, post-synaptic, modulatory) via meta-training on memory tasks.
• Exponential Moving Average (EMA) prototyping (Yu et al., 3 Nov 2025): Memory slots maintain running key prototypes via $c_i^{(t)}$ and $n_i^{(t)}$, yielding $k_i^{(t)}=c_i^{(t)}/n_i^{(t)}$.
• One-shot calibration (Shutova et al., 31 Jan 2025): Adapter/predictor parameters are fitted by ridge regression over calibration data, enabling residual quantization of unpredictable components and hybrid compression.
• Entropy-based splitting (Li et al., 2018): Adaptive creation of memory banks is triggered by entropy estimates, regularized against expected distributions to avoid over-fragmentation.
• Adaptive block-size search and segment statistics (Chen et al., 26 Oct 2025): Internal block sizes are chosen via search over semantic fidelity ratios in each segment, with preference for the largest non-fragmenting block size meeting utility constraints.

These regimes ensure self-organization of banks as the complexity or resource constraints change, supporting continual learning and robustness.

5. Comparative Performance and Trade-Offs

Empirical analysis demonstrates adaptive methods often outperform static or naïve approaches:

Method	Mem Budget	Accuracy	Speedup	Comment
SCOPE(Slide)	12.5%	–3pts vs Full	+25.9%	O(T)→O(1), constant-size decoding (Wu et al., 18 Dec 2024)
SABlock	96 entries	99.9%	Up to 9.5×	Near full-cache at extreme compression (Chen et al., 26 Oct 2025)
Adaptive LFU (VOS)	2 frames	≈full	30–80%	Matches every-k, outperforms first+latest (Pourganjalikhan et al., 2022)
Ada-KV (LLM)	128h	+1.6 pts	—	Adaptive head-budget dominates uniform (Feng et al., 16 Jul 2024)
MadaKV (multimodal)	20%	–0.37 pts	1.42×–1.62×	95% memory reduction, head-wise compensation (Li et al., 6 Jun 2025)
AQUA-KV (LLM)	2-2.5 bits	<1% drop	>7×	State-of-the-art quantization (Shutova et al., 31 Jan 2025)

As shown, adaptive selection (by attention, modality, entropy, or usage) yields memory savings and decoding speedups with minimal quality loss. Banks supporting dynamic redundancy (Kleyko et al., 2022), bank-splitting (Li et al., 2018), and multimodal adaptation (Li et al., 6 Jun 2025) further enhance scaling and task fidelity.

6. Applications and Extensions

Adaptive key-value banks are central in:

• Long-context LLM inference (Wu et al., 18 Dec 2024, Chen et al., 26 Oct 2025, Gu et al., 4 Jun 2025, Feng et al., 16 Jul 2024, Li et al., 6 Jun 2025, Shutova et al., 31 Jan 2025): Enabling efficient prefill/decoding cache management, block-wise semantic retention, per-head modality-aware eviction, and hardware-scale quantization.
• Biologically plausible memory models (Tyulmankov et al., 2021): Demonstrating competitive auto-/hetero-associative recall versus Hopfield networks, with extensions to continual and sequence learning via slot cycling and decay.
• Multimodal systems (Li et al., 6 Jun 2025): Head-and-modality wise cache pruning in vision-LLMs, hierarchical compensation for fidelity at deep layers.
• Explicit knowledge tapes (Yu et al., 3 Nov 2025): Enabling updatable, interpretable, and high-throughput knowledge storage for fact-intensive and low-data settings.
• Hardware-in-memory computing (Kleyko et al., 2022): Dynamic redundancy tuning for error compensation in non-volatile memory arrays (PCM), without neural retraining.
• Video object segmentation (Pourganjalikhan et al., 2022): Real-time memory pruning for arbitrary-length VOS tasks, overcoming scaling bottlenecks of naïve frame storage.
• Disaggregated persistent memory stores (Lee et al., 2022): Adaptive caching and lock/log-free indexing for distributed key-value stores under high-skew, fast reconfiguration.

A plausible implication is that adaptive key-value memory banks will continue to form the backbone of scalable, efficient neural and cognitive architectures, extending beyond unimodal text to multimodal, lifelong learning, and distributed serving environments.

7. Limitations and Directions for Future Research

Limitations arise from the coarse granularity (e.g., head- or segment-level adaptation (Feng et al., 16 Jul 2024, Li et al., 6 Jun 2025, Chen et al., 26 Oct 2025)), the proxy nature of attention-based importance scores, and challenges in dynamic multimodal scaling (e.g., video/audio, >34B parameters (Li et al., 6 Jun 2025)). Methods relying on recent-window proxies may need further enhancement for adversarial or non-stationary contexts (Feng et al., 16 Jul 2024). Extensions include joint layer-head adaptation, learned token-importance models, and integration with quantization or low-rank approximations for tighter compression (Gu et al., 4 Jun 2025, Feng et al., 16 Jul 2024, Shutova et al., 31 Jan 2025). Cross-modal banks and dynamic hardware adaptation suggest further research for deployment in real-world, resource-constrained environments.

In sum, adaptive key-value memory banks represent an integrative solution to the longstanding tension between capacity, efficiency, robustness, and semantic fidelity in neural and cognitive computing systems.