Self-Updatable Memory Pools

• Self-updatable memory pools are dynamic architectures that allow for controlled post-deployment updates, supporting continual learning and system resilience.
• They separate static core logic from dynamic memory segments, enabling rapid updates, controlled forgetting, and efficient parameter management.
• Applications in neural models (MemoryLLM) and cloud systems (Vmem) demonstrate improvements in performance, scalability, and operational stability.

Self-updatable memory pools are a class of architectural and algorithmic solutions that permit components or systems to dynamically update, augment, and manage a pool of memory or parameters post-deployment, either for purposes of continual learning (as in neural models) or for online reliability, elasticity, and maintainability (as in high-availability cloud infrastructure). Recent advances span domains ranging from transformer-based neural architectures with “latent” memory tokens to OS-level cloud server memory management with live upgradeability. Implementations prioritize low-overhead, fine-grained updatability, operational stability under high rates of change, and preserved or improved task performance.

1. Architectural Principles and Core Design

Self-updatable memory pool designs separate the “static” core logic from memory regions or parameter pools that admit controlled, dynamic updates without disruptive retraining or rebooting. Two primary instantiations are prominent.

In neural architectures (MemoryLLM):

A transformer (e.g. Llama2-7B, denoted φ) is augmented with a set of fixed-size memory token pools θ, where, for each transformer layer $l\in\{1,\dots,L\}$, the memory pool consists of $N$ tokens $\theta_l \in \mathbb{R}^{N\times d}$. At inference (“generation”), the model’s hidden states attend jointly to themselves and all memory tokens. At update (“self-update”), only a subset of the memory pool is processed to integrate new information, inducing an approximately exponential decay over older memories. No gradients are used for the update step at inference time, supporting rapid, low-latency inserts and deletions (Wang et al., 2024).

In cloud infrastructure (Vmem):

A reserved physical memory pool ($M_{\mathrm{reserved}}$) is managed by a two-module kernel architecture. The stable interface (vmem.ko) exposes device semantics (e.g., /dev/vmem) while the core logic module (vmem_mm.ko) manages allocations, reservations, and fast mapping. Hot-upgradeability is enabled via atomic function pointer swaps and RCU-protected mechanism; update and replacement of core logic occur without interruption of service, and existing allocations remain valid across versions (Zheng et al., 13 Nov 2025).

2. Formal Definition, Data Structures, and Update Algorithms

MemoryLLM Formalization:

• For each layer $l$, $\theta_l \in \mathbb{R}^{N\times d}$ is trainable; initialization is random or from a pre-trained pool.
• At inference, attention is over $[h_l; \theta_l]$:

$$Q = h_l W_Q,\quad K = [h_l; \theta_l] W_K,\quad V = [h_l; \theta_l] W_V$$

Output is $$h_{l+1} = \operatorname{softmax}\left(\frac{QK^T}{\sqrt{d}}\right)V$$.

• At update:
  1. Extract last $K$ memory tokens $N$0.
  2. Concatenate with $N$1, propagate through $N$2.
  3. The resulting $N$3 outputs replace $N$4 randomly selected old memory tokens, maintaining pool size $N$5.

Vmem Formalization:

• Reserved memory $N$6 is divided into $N$7MB slices, tracked per-NUMA node by a $N$8-byte array.
• Each VM maintains a “fastmap” structure for rapid virtual-to-physical address translation, with each entry mapping a contiguous memory segment.
• Hot-upgrade proceeds by atomically swapping interface function pointers, iteratively updating VMA/file ops via RCU, and updating reference counts for safe core module unload.

Pseudocode Example (MemoryLLM self-update):

$l$6 (Wang et al., 2024)

Pseudocode Example (Vmem hot-upgrade):

$l$7 (Zheng et al., 13 Nov 2025)

3. Training Objectives, Performance Metrics, and Evaluation Protocols

MemoryLLM (Neural Model)

• Training Losses:
  • $N$9: Next-token prediction after self-update with gradient/no-gradient over new memory insert.
  • $\theta_l \in \mathbb{R}^{N\times d}$0: Sequential document update, measuring long-range integration.
  • $\theta_l \in \mathbb{R}^{N\times d}$1: Alternating main/side document updates to quantify controlled forgetting.
• Evaluation:
  • Model editing: ZsRE and CounterFactual benchmarks, reporting efficacy, generalization, specificity, and harmonic mean (score).
  • Long-context QA: LongBench, F1 vs. context lengths up to $\theta_l \in \mathbb{R}^{N\times d}$2k.
  • Retention: SQuAD, NaturalQA; measure accuracy after repeated unrelated updates compared to the exponential decay bound:

  $\theta_l \in \mathbb{R}^{N\times d}$3 - Integrity: Accuracy on just-injected items after $\theta_l \in \mathbb{R}^{N\times d}$4 updates to detect drift or catastrophic forgetting.

Vmem (Cloud Memory Pool)

• Performance Metrics:

  • Sellable memory increase ($\theta_l \in \mathbb{R}^{N\times d}$5).
  • VM boot time (e.g., $\theta_l \in \mathbb{R}^{N\times d}$6GB: $\theta_l \in \mathbb{R}^{N\times d}$7s Hugetlb vs. $\theta_l \in \mathbb{R}^{N\times d}$8s Vmem).
  • Network throughput ($\theta_l \in \mathbb{R}^{N\times d}$9 on DPU-accelerated VMs).
  • Metadata overhead ($M_{\mathrm{reserved}}$0MB vs $M_{\mathrm{reserved}}$1GB on $M_{\mathrm{reserved}}$2GB host).
• Upgrade Latency:
  • Mean $M_{\mathrm{reserved}}$3s, $M_{\mathrm{reserved}}$4th percentile $M_{\mathrm{reserved}}$5s per hot-upgrade event.

4. Comparative Quantitative Results

Model / System	Benchmark	Legacy Baseline	Prior Art	Self-updatable Pool Variant
MemoryLLM-7B	ZsRE (score)	Llama2-7B: 55.6	ROME: 69.3	79.2
MemoryLLM-7B	CounterFactual (score)	Llama2-7B: 20.7	ROME: 69.2	75.3
MemoryLLM-7B	Knowledge retention (a₁)	—	—	SQuAD: 0.80 / NatQA: 0.75
MemoryLLM-7B	After 20 unrelated updates	—	—	Accuracy: ~0.50 (bound ~0.53)
Vmem	Sellable memory (ΔM)	—	—	~2% increase
Vmem	VM boot, 373GB	100 s (Hugetlb)	—	0.6 s
Vmem	Hot-upgrade latency	—	—	2.1–3.5 μs
Vmem	Metadata overhead (384GB host)	6 GB (struct page)	—	5 MB (<0.0013× OS)

(Wang et al., 2024, Zheng et al., 13 Nov 2025)

5. Hyperparameters, Scalability, and Trade-Offs

MemoryLLM (hyperparameters for 7B backbone):

• Layers $M_{\mathrm{reserved}}$6, hidden size $M_{\mathrm{reserved}}$7, $M_{\mathrm{reserved}}$8 memory tokens per layer, $M_{\mathrm{reserved}}$9 slots updated per input, $l$0.
• Update frequency: once per paragraph. Pure insertion/deletion at inference (no learning rate).

Vmem (scaling characteristics):

• Slice tracking per node: $l$1MB granularity. Metadata: $l$2120B per VM, $l$3B per segment.
• Overhead grows linearly with VM count and slice count; remains $l$45MB on large hosts (e.g. 300,000 servers, hundreds of millions of VMs).
• Upgrade overhead remains $l$5s-scale. Data structure compatibility must be rigorously maintained; fields added only with reserved padding, limiting flexibility for large structural changes.

Trade-offs include explicit forgetting (MemoryLLM, via replacement and exponential decay), complexity in testing hot-swap code paths (Vmem), limited extension flexibility for complex changes, and concurrency contention (Vmem) during upgrade.

6. Application Domains and Significance

LLMs and Continual Learning:

Self-updatable memory pools such as those in MemoryLLM enable post-deployment injection of new knowledge and long-term information retention, thus bridging the gap between static pre-trained models and dynamically updatable knowledge bases. Empirically, such models surpass existing architectural and model-editing baselines on efficacy, generalization, specificity, and integrity after repeated updates (Wang et al., 2024). This supports scalable, practical deployment in settings with evolving knowledge requirements.

Cloud Infrastructure:

Vmem demonstrates how self-updatable memory pools can enable production cloud platforms to maximize sellable memory, decrease VM start latency, improve network throughput, and support live module upgrades without disrupting running VMs. The separation of a stable interface and a swappable core logic module, combined with fast metadata and mapping techniques, supports highly elastic, stable, and scalable operations in environments serving hundreds of millions of VMs (Zheng et al., 13 Nov 2025).

These architectures illustrate convergent innovation in self-updatable pools for both AI systems and systems infrastructure, emphasizing design patterns of hierarchical separation, fine-grained memory tracking, upgrade-safe logic indirection, and lightweight metadata management.

7. Future Directions and Open Considerations

The design of self-updatable memory pools points to future work in several areas:

• For neural models: integration of more sophisticated memory selection/replacement policies, adaptive memory windowing, and mechanisms to mitigate information loss beyond exponential decay.
• For cloud platforms: increasing extension flexibility without sacrificing upgrade latency or memory overhead, enhancing compatibility across heterogeneous infrastructure, and further reducing contention during massive concurrent operations.

A plausible implication is that the separation-of-concerns and indirection techniques used in both LLM and cloud domains may serve as a template for designing further self-updating, low-downtime, and high-availability subsystems across a range of computational platforms. Current evidence demonstrates that with appropriate architecture and update strategies, self-updatable memory pools can jointly achieve high efficiency, flexibility, and operational stability (Wang et al., 2024, Zheng et al., 13 Nov 2025).