Dynamic Memory Architecture in Systems and AI

• Dynamic Memory Architecture is a system that adapts allocation, content, and access based on runtime requirements, user activity, and task context.
• It supports reconfigurable hardware controllers, dynamic software storage, and neural memory modules in distributed multi-agent and sequential processing systems.
• It incorporates formal feedback mechanisms and dual-memory models to balance efficiency, scalability, and operational correctness across diverse workloads.

A dynamic memory architecture refers to any physical or logical memory system—in hardware or software—that supports adaptation in allocation, content, access, or structure based on changing runtime requirements, user activity, or task context. This encompasses hardware memory controllers that reconfigure mapping, software storage systems that resize cache or reorganize data layouts, neural models that incorporate time-varying memory representations, and distributed or multi-user agent memory frameworks with evolving permission and sharing policies. Key distinguishing characteristics include explicit mechanisms for policy-driven or feedback-driven adaptation, systematic support for long-term and short-term memory processes, and mathematically formulated guarantees on access, capacity, or correctness.

1. Core Principles and Models

Dynamic memory architectures integrate formal mechanisms for runtime adaptation, often motivated by efficiency, scalability, or the need to reconcile competing demands (e.g., privacy vs. sharing, stability vs. flexibility). At their core, modern architectures build upon:

• Policy-driven access and partitioning: Memory is accessed or partitioned according to dynamic, often graph- or rule-based policies. As an example, in Collaborative Memory for LLM agents, permissions at any time $t$ are encoded as bipartite graphs $G_{U,A}(t)$ and $G_{A,R}(t)$, determining which user-agent-resource combinations are allowed (Rezazadeh et al., 23 May 2025).
• Dual/Hierarchical memory tiers: Systems maintain separate memory regions (or representations) for private vs. shared data, static vs. dynamic content, or fast vs. slow access. For example, Mem4D uses a dual-memory system with Transient Dynamics Memory (TDM) for recent, high-frequency motion details and Persistent Structure Memory (PSM) for long-term, globally consistent geometry (Cai et al., 11 Aug 2025).
• Time-varying control and adaptation: Allocation, retention, and access are controlled by feedback loops or dynamic mappings, e.g., dynamic resizing of caches in response to near-real-time metrics, as in DynIMS for HPC clusters (Xuan et al., 2016), or dynamic binary address mapping in DRAM controllers as in DReAM (Ghasempour et al., 2015).

These models are mathematically formalized through dynamic graphs, dual-memory update rules, feedback control laws, and provable theorems on access control or performance.

2. Software Systems: Dynamic Access Control, Resource Partitioning, and Multi-Agent Memory

Software-level dynamic memory architectures are prevalent in large-scale collaborative and multi-user AI systems:

• Collaborative Multi-Tier Memory: In multi-agent LLM ecosystems, memory is partitioned across private user fragments ($F^{private}$) and shared fragments ($F^{shared}$), with both tiers indexed and accessed according to dynamic, asymmetric permissions. Provenance attributes (contributing agents, resources, timestamps) are strictly tracked; every read/write is subject to policy projection/transformations ($T_{read}$, $T_{write}^{priv}$, $T_{write}^{sh}$) based on current permission graphs. Auditability and retroactive attestation are guaranteed since every fragment’s provenance is immutable and every access check is replayable on archived graphs (Rezazadeh et al., 23 May 2025).
• Dynamic Memory Controllers for Resource-Constrained Systems: In data-intensive HPC workloads, the DynIMS architecture uses sub-second interval monitoring of per-node RAM usage, real-time stream processing, and feedback regulation to dynamically resize in-memory storage allocations according to compute demand, optimizing throughput while preserving compute job QoS. A feedback control law

$U_{i+1} = U_i - \lambda \frac{r_i - r_0}{r_0}$

enforces convergence, with experimental gains of up to $5\times$ in mixed Spark/HPCC workloads (Xuan et al., 2016).

• Processing-in-Memory (PIM) Allocators: PIM-malloc eliminates the host-PIM metadata transfer bottleneck through fully distributed, per-core dynamic allocation with hierarchical (thread-cache + buddy) data structures. A hardware-accelerated metadata cache further reduces mean allocation latency ($66\times$ software, $31\%$ with HW assist) and dramatically increases throughput in dynamic graph workloads [$28\times$, (Lee et al., 19 May 2025)].

3. Neural and Cognitive Dynamic Memory Architectures

Modern sequence modeling, planning agents, and learning systems use explicitly dynamic, trainable memory modules:

• Dynamic Memory Networks (DMN/DMTN): These architectures feature modular, differentiable memory systems in which input “facts” are encoded, questioned, and episodically attended over multiple passes using trainable gating mechanisms. The Dynamic Memory Tensor Network (DMTN) replaces hand-crafted attention gates with neural-tensor-based multiplicative scoring, yielding superior performance, especially under weak supervision (e.g., $18/20$ tasks passed vs. vanilla DMN’s $6/20$ on bAbI-1K) (Ramachandran et al., 2017, Kumar et al., 2015).
• Cognitive Hierarchies and Experience Sharing: In SMITH, agent memory is organized into procedural, semantic, and episodic stores. Dynamic tool creation is framed as iterative code-generation within a sandbox, while cross-task sharing leverages dense trajectory embeddings, semantic similarity, and curriculum scheduling. The architecture achieves state-of-the-art generalist agent results (Pass@1 = $81.8\%$ on the GAIA benchmark) (Liu et al., 12 Dec 2025).
• Dynamic Energy-Based Sequential Memory: The EDEN model establishes a two-timescale dynamic energy system, combining a static high-capacity associative engine (modern Hopfield) with a slow, asymmetrically-coupled context (“ramping cells”) population. This yields exponential sequence-memory capacity $O(\gamma^N)$ and analytic phase-boundary criteria between static and dynamic recall, mirroring observed transitions in biological memory (Karuvally et al., 28 Oct 2025).

4. Hardware and Physical Layer Innovations

Dynamic memory architectures extend to physical devices and low-level memory systems:

• Dynamic Re-Addressing in DRAM: DReAM dynamically monitors per-bit address change rates, thereby updating the physical→structural mapping (bitfield-to-row/bank/channel assignments) on-the-fly. Workload-specific mappings are proposed based on bit-entropy, with data migrated only on first access after a mapping change. Empirical speedups reach up to $28\%$ on mapping-sensitive applications (Ghasempour et al., 2015).
• Dynamically Augmented SRAM Storage: Augmented Memory Computing introduces 8T dual-bit and 7T ternary-bit SRAM cells, which can switch modes to store static SRAM-style bits or, under control signals, extra DRAM-style (volatile) bits. The 8T cell achieves $2\times$ capacity in Augmented mode with $25\,\mu$s retention, and integrates directly with in-memory compute schemes for increased throughput and energy efficiency (Sheshadri et al., 2021).
• Memcomputing with Dynamic Capacitive Cells: DCRAM employs memcapacitive cells with dynamically tunable state variables. By synchronizing voltage pulses and inter-cell coupling, DCRAM supports polymorphic in-memory digital logic, achieving energy budgets of $\sim$1–5 fJ per operation and nanosecond-scale logic execution, all in a CMOS-compatible flow (Traversa et al., 2013).

5. Distributed, Multi-Agent, and Hierarchical Dynamic Memory

Emerging large-scale AI and robotics systems demand cross-layer memory architectures that adapt collectively across compute, communication, and deployment:

• Self-Evolving Distributed Memory Architectures (SEDMA): SEDMA unifies memory management in distributed AI across three architectural layers: (1) computation (dynamic partitioning and error correction for RRAM arrays), (2) communication (memory-aware, adaptive peer selection based on LTM/STM tracking), and (3) deployment (real-time, memory-guided task placement and continuous reoptimization). The dual-memory system (short-term/long-term) enables runtime adaptation while preserving operational history. Key performance metrics include $87.3\%$ memory utilization efficiency, $142.5$ ops/s ($\sim 45\%$ over Ray Distributed), and $30\%$ reduction in communication latency (Li et al., 9 Jan 2026).
• Scene Graph Memory for Dynamic Environments: In embodied AI, Scene Graph Memory (SGM) aggregates all partial, time-indexed observations into a single, feature-rich dynamic graph structure, supporting joint link prediction and efficient search in partially observable, semantically changing environments. This mechanism supports robust adaptation to novel, dynamic settings and outperforms standard GNNs and heuristic baselines on dynamic object-location tasks (Kurenkov et al., 2023).

6. Dynamic Memory in Sequential Reasoning, Visual Processing, and Dialogue

Dynamic memory architectures drive advances in sequential reasoning, object tracking, scene understanding, and dialogue management:

• Cascade and Dual-Memory for Sequential Processing: HyMem combines a summary-level (fast, low-cost) memory layer and a raw-text (deep, high-fidelity) memory with dynamic retrieval scheduling based on query difficulty and similarity thresholds. Only complex queries trigger deep retrieval and LLM-based iterative reasoning. This design achieves strong performance while reducing computational cost by $92.6\%$ on long-context benchmarks (Zhao et al., 15 Feb 2026).
• Specialized Memory Modules in Perception: Mem4D decouples static geometry from dynamic motion via TDM and PSM, alternating queries at every fusion stage to balance high-fidelity motion retention and drift-free global structure in online 3D scene reconstruction tasks. The approach achieves a new online metric-depth record (AbsRel $0.846$ on Sintel, $21.6\%$ better than CUT3R) and high pose accuracy at real-time speeds (Cai et al., 11 Aug 2025).
• Dynamic Memory Networks for Visual Tracking: Real-time dynamic object trackers employ an external memory block, indexed and updated via learned attention and LSTM-gated control, to adaptively preserve past object templates. This allows feedforward, online adaptation without expensive retraining and supports scalable long-term retention (Yang et al., 2018).

7. Theoretical Guarantees and Empirical Outcomes

Dynamic memory architectures are often accompanied by formal theorems and empirical validations:

• Provable Access Guarantees: Collaborative Memory provides an explicit theorem that any memory fragment returned at time $t$ automatically satisfies the current user-agent-resource access policy. Provenance tracking of each fragment enables full retrospective audit, supporting both compliance (e.g., GDPR) and interpretability in LLM workflows (Rezazadeh et al., 23 May 2025).
• Analytic Phase Transitions: In energy-based sequential memory, EDEN admits a closed-form solution for regime boundaries and dwell/escape times, connecting memory stability with the ratio $r=\alpha_s/\alpha_c$ and establishing sharp capacity transitions (Karuvally et al., 28 Oct 2025).
• Scalability and Efficiency: Dynamic memory architectures such as SEDMA and PIM-malloc demonstrate significant improvements in resource utilization, throughput, and latency by coordinating memory control across layers and exploiting task and workload statistics (Li et al., 9 Jan 2026, Lee et al., 19 May 2025).

Taken together, dynamic memory architectures represent a class of systems defined by principled, mathematically explicit adaptation of memory access, allocation, and sharing policies in response to evolving operational context, with strong empirical and theoretical support across software, hardware, and cognitive domains.