Cognitive Layered Memory Architecture (COLMA)

• COLMA is a formal framework for modeling memory in intelligent systems, integrating cognitive psychology, neuroscience, and AI for robust associative recall and continual learning.
• It leverages dynamic adaptability, multimodal integration, and hierarchical organization to mirror human memory processes and enhance artificial general intelligence.
• The architecture employs layered memory encoding, retrieval, and attention mechanisms to support dynamic reasoning, continuous consolidation, and multi-agent collaboration.

A Cognitive Layered Memory Architecture (COLMA) is a formal framework for modeling memory in intelligent systems, designed to emulate the layered, adaptive, and multimodal nature of human memory. By integrating structural principles from cognitive psychology, neuroscience, and modern AI research, COLMA enables systems to support robust associative recall, dynamic reasoning, continual learning, and cross-modal information fusion. It stands as a pivotal architecture for advancing artificial general intelligence (AGI), supporting both the traceable consolidation of knowledge and flexible recall under real-world constraints.

1. Foundational Principles and Design Rationale

COLMA synthesizes fundamental principles derived from cognitive scenarios and human memory research (Cai et al., 16 Sep 2025). The core design tenets include:

• Dynamic Adaptability: Continuous memory update and consolidation processes reflecting ongoing learning and real-time revision.
• Multimodal Integration: Fusion of diverse sensory, symbolic, and contextual data, supporting cognitive tasks that require heterogeneous input sources (e.g., visual, tactile, structured knowledge) (Cai et al., 16 Sep 2025).
• Hierarchical Organization: Layered construction emulating sensory, short-term, and long-term memory stores; correspondence with Atkinson–Shiffrin models and neuroanatomical insights (Kotseruba et al., 2016).
• Reasoning Transparency: Explicit coordination between memory layers to provide interpretable retrieval, consolidation, and decision cycles.
• Scenario-Driven Operations: Top-layer algorithms tailored to practical cognitive task requirements, from daily recall to complex decision cycles.

This layered approach is informed by empirical studies on memory phenomena such as list length effect, fan effect, and false memories (Cao et al., 21 Sep 2025), and supports both symbolic and distributed (vector-based) memory representations.

2. Layered Structure and Functional Components

The architecture is typically instantiated over five hierarchical layers (Cai et al., 16 Sep 2025), with structural and functional assignments as follows:

Layer	Components and Roles	Biological Analogue
User Scenario Layer	Scenario-specific reasoning, recall, adaptation	Prefrontal/cognitive control
Functionality Layer	Core cognitive functions: reasoning, prediction, association	Executive networks
Coordination Layer	Synchronization of different store types, dynamic updating	Hippocampo–cortical interface
Knowledge Category Layer	Multimodal knowledge fusion (graphs, vectors, symbolic facts)	Neocortex integration
Physical Persistence Layer	High-throughput, distributed storage	Biological persistence (memory)

The Coordination Layer orchestrates flows among short-term, medium-term, and long-term memory (denoted as $M_s$, $M_m$, $M_l$), incorporating update rules such as:

$$M_l^{new} = M_l^{old} + \eta\, (f(M_s, M_m) - M_l^{old})$$

where $\eta$ is a learning rate and $f(\cdot)$ is an integration function merging temporary and intermediate representations for stable consolidation.

3. Memory Encoding, Retrieval, and Attention Mechanisms

Memory objects are encoded as wide digital vectors capturing thousands of features (e.g., sensory, emotional, semantic), with associative recall based on feature matching (0805.3126):

$\mathbf{m} = (x_1, x_2, ..., x_N)$

Recall employs pseudorandom cue selection driven by a cue editor (shift-register/XOR logic), enabling high-bandwidth, non-exhaustive retrieval:

$\mathbf{c}' = \text{ShiftRegister}(\mathbf{c})$

Both bottom-up and top-down feedback are integrated in hierarchical associative memory systems, with recurrent dynamics ensuring both pattern completion and rich attractor assembly (Krotov, 2021). Attention is dynamically directed by subliminal analysis, wherein recalled images compete for cognitive focus based on their computed importance index:

$I = \alpha B + \beta E + \gamma M + \delta R$

with $B$ (brightness), $E$ (emotional strength), $M$ (matched cues), and $R$ (recency) contributing weighted significance.

4. Practical Instantiations and Evolutionary Scaling

Robust hardware instantiations are realized through hierarchical modular networks built from resistive memory cells with no crossover wiring, enabling efficient character recognition under heavy noise and deformation (James et al., 2012). Each cell executes:

$y = f\left( \sum_i \frac{V_i}{R_i} - V_T \right)$

and is trained with staged evolutionary algorithms (selection, genetic crossover, and targeted fine-tuning) for optimal output code spacing and error minimization.

In robotic cognitive architectures, a distributed, event-driven episodic memory system centralizes active multi-modal data and supports compression into latent representations for prediction and simulation (Peller-Konrad et al., 2022). Performance is validated by metrics such as PSNR ($46$–$52$ dB for reconstructions, $32$–$36$ dB for forward prediction).

Advanced LLM-based agents utilize layered memories with decay-controlled retrieval across short-, middle-, and long-term scales, computing recency, relevancy, and importance for each historical event (Equations 1–4) (Li et al., 2023). Multi-agent systems leverage individualized traits and inter-agent debate for improved robustness.

5. Symbolic–Distributed Synergy and Chunking Processes

COLMA architectures increasingly integrate dual-process frameworks, combining fast distributed (implicit) memory—for intuition, similarity-based judgment, and associative learning—with slow symbolic (explicit) reasoning for systematic rule following (Sun, 26 Oct 2024). In enhanced Clarion–L models, LLMs implement bottom-layer intuition, while explicit symbolic modules manage deliberative reasoning:

• Bottom–Up Activation: Responses from the implicit layer can trigger chunk nodes for explicit processing.
• Top–Down Activation: Symbolic knowledge modulates and steers distributed representations.

Chunking processes in symbolic neural networks efficiently aggregate sequences of object–effector–source triples into reusable high-level chunks, optimizing retrieval and context-based adaptation (Greer, 2020).

6. Lifelong Learning, Multi-Agent Collaboration, and Robustness

COLMA architectures support lifelong and collaborative learning by:

• Iterative consolidation cycles: Cyclic retrieval, comparison, and reconsolidation to avoid catastrophic forgetting.
• Multi-agent knowledge sharing: Distributed collaboration via shared, dynamically updated memory traces, promoting collective intelligence (Cai et al., 16 Sep 2025).
• Scenario-driven generalization: Scenario mapping from mushroom identification to mathematical problem solving drives context-relevant memory operations.
• Robustness and scalability: Experimental validation on long-horizon dialogue datasets (LoCoMo, Robotouille) confirms improved recall rates, dialogue coherence, and token efficiency (latency $1.309$ s vs $3.931$ s for baselines) (Vishwakarma et al., 9 Jun 2025, Zhang et al., 21 Aug 2025).

Decay and interference mechanisms mirror cognitive phenomena such as list length and fan effects (Cao et al., 21 Sep 2025). Layered systems blend fast cache-like storage with associative retrieval, balancing precision, generalization, and resilience to noise (nonsense effect).

7. Comparison with Existing Architectures and Impact on AGI

Distinct from isolated memory augmentations (parameterized in-context windows, vector databases, or knowledge graphs), COLMA provides:

• Holistic integration: Multimodal fusion, cross-layer synchronization, and interpretability mechanisms (Cai et al., 16 Sep 2025).
• Biological plausibility: Hierarchical organization mimicking hippocampo-cortical interplay, chunked assembly, and top–down/bottom–up feedback (Kotseruba et al., 2016).
• Scalable, evolving infrastructure: Adaptability to changing AGI requirements, robust multi-agent collaboration, and scenario-driven intelligence.

The cumulative effect is a structured, generalizable memory architecture foundational to artificial general intelligence development.

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COLMA encapsulates a paradigm shift in AI memory systems, unifying layered associative, symbolic, and distributed mechanisms to equip modern intelligent agents with robust lifelong cognitive capabilities, interpretability, and adaptability under realistic, multimodal scenarios.