Self-Organizing Memory Architectures

• Self-organizing memory architectures are systems where memory dynamically organizes through local interactions and decentralized mechanisms without central control.
• They integrate diverse approaches, including MAPE loops, Hebbian learning, and physical criticality, to optimize performance in digital, neural, and material-based systems.
• These architectures achieve tangible benefits such as 25% latency reduction and minimized catastrophic forgetting, enabling robust and scalable memory in hardware and AI applications.

Self-organizing memory architectures are computational or physical systems in which information storage, organization, and adaptation arise dynamically from local interactions or decentralized algorithms, rather than being imposed by globally orchestrated controllers or hand-designed layouts. These architectures span a broad spectrum: from decentralized hardware memory managers in manycore processors, to neural and cognitive models for continual learning, to associative and physical substrates inspired by biological memory systems. The following sections detail core concepts and representative architectures, spanning digital, neural, materials, and distributed system implementations, with technical fidelity to published models and empirical results.

1. Decentralized Digital and Hardware Memory Architectures

A fundamental approach to self-organizing memory is the decentralization and local autonomy of memory management, as seen in the Self-aware Memory (SaM) architecture (Mattes et al., 2014). In SaM, global memory is partitioned into distributed modules, each responsible for its own physical memory region, mapped to DRAM and interfaced via service-oriented APIs. No central memory controller or global directory is present. Instead, local Memory Modules (SaM-Mem) autonomously manage memory allocation, access controls, error correction, and local load statistics, while each compute core has a local Management Agent (SaM-CPL) that translates addresses and arbitrates access or migration.

Self-optimization proceeds via a decentralized MAPE loop — Monitor, Analyze, Plan, Execute — augmented with threshold-based triggers and a lightweight, consensus-driven planner. Each module continuously monitors its usage, aggregates local and neighbor state, applies thresholding for optimization candidates (e.g., exceeding local load, ECC errors), and executes migration or load balancing if local cost-benefit analyses are positive:

• Benefit $B_p = f_p (L_\text{old}-L_\text{new})$
• Cost $C_p = S_p \beta + \sigma$
• Migration occurs if $B_p \geq C_p$

Distributed actions, such as page migration, involve two-phase voting among source, target, and control agents. The architecture achieves amortized overhead: for typical parameters ($\Delta m = 5000$, $R=1$, $T=45$), SaM reduces access latency by ~25% via $\sim$250 page migrations, with total overhead $O\approx0.08\,\text{ms}$ and benefit $R\approx0.12\,\text{ms}$ per application ms, i.e., a 50% amortization (Mattes et al., 2014).

The SAPA architecture (Self-Aware Polymorphic Architecture) advances this paradigm by integrating reinforcement learning and control-theoretic local loops in its Approximation-Aware Memory Organization Models (AMOM). Here, each memory slice manages migration and replication based on local counters, power and QoS metrics, and block-level Q-learning, promoting or demoting blocks in the hierarchy in a non-coherent, self-organized fashion (Kinsy et al., 2018).

2. Neural and Cognitive Self-Organizing Memory Models

Neural architectures extend self-organization to memory by leveraging local plasticity, unsupervised competition, and task-free continual adaptation. The Continual Self-Organizing Map (CSOM) (Vaidya et al., 2024) generalizes the classical Kohonen SOM by augmenting each unit with running variance, hit counts, per-unit learning rates, and local parameter decay. The CSOM update rule normalizes incoming sample distances by per-unit variance, ensuring that underutilized (high-variance) units remain plastic and readily recruited for novel patterns, while heavily used units become stabilized:

$$M_i(t+1) = M_i(t) + \phi_i(t) [x(t) - M_i(t)], \quad \Omega^2_{d,i}(t+1) = \lambda_\omega \Omega^2_{d,i}(t) + (1-\lambda_\omega)[x_d(t) - M_{d,i}(t)]^2$$

This mechanism allows CSOM to handle concept drift and catastrophic forgetting without explicit task boundaries or replay, maintaining high accuracy (ACC ≈ 75–85% on continual class-incremental tasks) and reducing forgetting (FM ≈ 7–13%) compared to vanilla SOM or buffer-based methods.

Hybrid neural-architectures, such as combining SOMs with MLPs (Bashivan et al., 2019), use an online-adaptive SOM to partition and nonlinearly gate the hidden units of a feedforward net. This structuring ensures that parameter regions specialized for prior tasks are preserved, and only mutually-activated subnetworks receive updates, thus greatly mitigating interference and catastrophic forgetting, approaching the performance of replay-based methods but without needing explicit buffer storage.

Biologically grounded models structure episodic memories into higher-level cognitive maps by combining local Hebbian updates, successor-feature estimations, and hierarchical clustering, as in (Dzhivelikian et al., 29 Sep 2025). A Level 1 HMM captures episodic transitions; clusters are merged online using Hebbian rules and successor feature similarity, resulting in an emergent Level 2 cognitive map. All plasticity is local (no backpropagation); convergence yields high cluster purity and efficient abstraction. This mechanism is shown to link hippocampal and entorhinal representations to concrete algorithmic updates.

3. Physical and Materials-Based Self-Organizing Memory

At the hardware and materials level, self-organizing memory is realized by exploiting collective nonlinear dynamics, phase transitions, and local adaptive rules intrinsic to physical substrates. Self-Organising Memristive Networks (SOMNs) (Caravelli et al., 31 Aug 2025) embody this paradigm, using nanowire or nanoparticle assemblies in which each junction acts as a memristive device with local ionic-filament or tunneling dynamics:

$C_p = S_p \beta + \sigma$0

Where $C_p = S_p \beta + \sigma$1 is the filament or gap state, and all junctions are interdependent via Kirchhoff constraints. Networks operate near dynamical critical points, giving rise to phase transitions and self-organized criticality: avalanches and scale-free activity propagate through the array, mirroring aspects of biological neural plasticity (Caravelli et al., 31 Aug 2025, Baulin et al., 11 Nov 2025).

Learning and memory emerge physically via two routes:

• Reservoir computing: input voltages drive high-dimensional transient currents, information is read out via a trained linear map; the physical system itself does not need to be fine-tuned, only the readout.
• Contrastive (Hebbian/anti-Hebbian) learning: repeated feedback pulses reinforce or diminish specific current pathways, enabling associative memory and continual learning without catastrophic forgetting.

Memory retention is determined by material timescales: short-term memory via volatile dynamics, long-term memory via stable rewiring (e.g., permanent filaments or breakages). Storage capacity is estimated at $C_p = S_p \beta + \sigma$2 retrievable patterns for $C_p = S_p \beta + \sigma$3 junction devices. Energy efficiency can surpass CMOS—operation at $C_p = S_p \beta + \sigma$410 μW and switching energies down to sub-femtojoule per event have been reported (Caravelli et al., 31 Aug 2025).

Material-based intelligence (Baulin et al., 11 Nov 2025) generalizes the self-organization principle: information is stored in physical state variables (concentrations, stresses, defect networks) and is written and read via local dynamics governed by reaction–diffusion, variational phase fields, network attractors, or active matter flows. Robust, replicable, and adaptable memory arises from phase transitions (multistability), pattern formation, and the maintenance of these states dissipatively far from thermodynamic equilibrium.

4. Distributed, Data-Centric, and Programmable Matter Architectures

Self-organization of memory is a foundational mechanism in distributed and data-centric computing architectures. PiNVSM (Dubeyko, 2019) dispenses with the von Neumann model by associating every data portion with a Data Processing Unit (DPU) that co-locates non-volatile memory and processing logic. Data items carry keyword tags, are mapped to “home” DPUs via hash or semantic proximity, and DPUs periodically rebalance workload and data placement through a decentralized protocol:

$C_p = S_p \beta + \sigma$5

$C_p = S_p \beta + \sigma$6

Self-organization is accomplished by local exchanges: DPUs with excess load migrate items to neighbors with lower load and higher tag overlap. Over time, high-affinity items cluster, data-access locality improves (mean lookup hops fall from 3.2 → 0.4 in simulation), and throughput increases by 5–10 × (Dubeyko, 2019).

Particle-based programmable matter systems extend this principle to ensembles of constant-memory, locally communicating agents that organize themselves physically (on a lattice) into memory structures (lines, grids, counters) and perform collective computation (Porter et al., 2017). Local rules drive the recruitment and state changes of particles, enabling robust assembly of distributed RAM for nontrivial computational tasks (counters, matrix–vector multiplication, image convolutions) without global coordination.

Self-Evolving Distributed Memory Architectures (SEDMA) (Li et al., 9 Jan 2026) generalize self-organization to multi-layered AI systems, tightly integrating computation (matrix partitioning on RRAM arrays), communication (memory-aware, peer-optimized routing), and deployment (adaptive agent placement). All layers are coupled via dual memory systems tracking both long-term performance patterns and short-term statistics. Optimization problems at each layer are solved with respect to dynamic cost/utility functions, and cross-layer feedback enforces continuous reconfiguration, yielding 87.3% memory utilization efficiency and robust adaptation under load shifts and failures.

5. Agent Memory and Cognitive Self-Organization for Long-Horizon Reasoning

Self-organizing memory architectures in the context of LLMs and agentic reasoning address the challenge of scaling memory to unbounded contexts or open-ended workflows. Architectures such as EverMemOS (Hu et al., 5 Jan 2026) and Nemori (Nan et al., 5 Aug 2025) implement explicit lifecycles for memory, inspired by biological and cognitive principles:

• Nemori employs a Two-Step Alignment Principle (event segmentation to produce coherent episodes) and a Predict-Calibrate Principle (surprisal-driven semantic consolidation guided by free-energy minimization), driving the emergence of both fine-grained episodic memory and abstract semantic knowledge through asynchrony and incremental self-organization. Nemori achieves state-of-the-art accuracy on memory-intensive QA benchmarks, outperforming longer-context and rule-based systems while using 88% fewer tokens.
• EverMemOS organizes conversational history into episodic MemCells, consolidates them into scene-level MemScenes via online clustering, and reconstructively recollects only the minimal context needed for query answering, leveraging multi-stage retrieval, foresight filtering, and sufficiency checking. Ablations confirm the lifecycle’s statistical necessity: removal of scene-level or episode-level organization degrades performance by 6–13% (Hu et al., 5 Jan 2026).

Both systems show that autonomous, self-organizing segmentation, abstraction, and retrieval—modulated by local surprise or contextual relevance—are crucial for tractable and adaptive long-horizon reasoning.

6. Formal Properties, Theoretical Guarantees, and Self-Organization in Associative Memory

Some self-organizing memory architectures emphasize formal and topological minimality. Universal Memory Architectures (Guralnik et al., 2015) employ snapshot structures: pairwise weights over a sensorium of Boolean features define weak poc sets (symbolic implication graphs with involution), and the median structure of the corresponding CAT(0) cubical complex yields the minimal, topology-recovering internal model space for an autonomous agent, learnable online with $C_p = S_p \beta + \sigma$7 space and time in the number of sensors.

Associative memory models, such as Dense Associative Memory (DAM) and its stochastic exponential generalization (SEDAM), exhibit temporal self-organization manifested as scale-free “neural avalanche” dynamics near criticality. In SEDAM (Cafiso et al., 16 Jan 2026), the energy landscape becomes increasingly rugged as memory load ($C_p = S_p \beta + \sigma$8) increases. For a finite noise window, the system self-organizes into complex regimes with power-law inter-event intervals, positive long-range autocorrelations, and anomalous diffusion exponents ($C_p = S_p \beta + \sigma$9), indicating nontrivial metastabilities. The critical noise window narrows with higher load but remains finite, illustrating “extended criticality” — a robust control region for self-organizing dynamics.

Property/Model	Mechanism/Principle	Key Evaluation
Self-aware Memory (SaM)	Decentralized MAPE + cons.	25% latency gain, 50% amort.
CSOM	Variance-normalized SOM	ACC 75–85%, FM 7–13%
EverMemOS	Lifecycle (episodic/scene)	86.76% LoCoMo, 83% LongMemEval
SOMNs	Physical criticality	Reservoir: 95% spoken digit rec.
SEDAM	Temporal complexity	Power-law avalanches, $B_p \geq C_p$0

7. Open Challenges and Future Directions

Constraints remain in energy dissipation, scalability, parameterless discovery of minimal ingredient sets for self-organization, and integration across material, neural, and software substrates. In hardware, device variability, mapping, and design tools present challenges. In software and neural systems, balancing plasticity and stability, managing memory allocation and retrieval, and abstracting beyond fixed granularity are active topics.

Material-based frameworks propose unifying free-energy, information-theoretic, and active-inference perspectives across scales for systematic design. Future architectures are expected to further intertwine physical self-organization, local learning, and hierarchical abstraction to enable robust, scalable, and adaptive memory in autonomous machines and AI agents (Baulin et al., 11 Nov 2025).

Self-organizing memory architectures thus realize autonomy, scalability, and adaptivity in both artificial and physical systems by leveraging local interaction rules, feedback, and emergent organization — foundational for the next generation of cognitive and intelligent computing platforms.