7 memory layers
Working, Short-Term, Episodic, Semantic, Procedural, Core, and Cross-Context Memory — each with its own time scale, persistence, and consolidation logic.
Track A
„How can AI remember, reason, and learn over time — without losing efficiency, reliability, or methodological transparency along the way?"
A seven-layer memory architecture grounded in neuroscience and tested in a production application.
Motivation
Today's large language models forget. Their context windows are technically bounded, and plain vector stores are not enough to faithfully model the layers of human memory — from a fleeting working memory to consolidated long-term knowledge.
What is missing are architectures that explicitly model consolidation, directed forgetting, confidence propagation, and predictive memory formation. This research answers with an architecture that did not emerge from marketing but from the question of how a system can maintain its own knowledge base to an academic standard.
Methodological approach
The architecture splits memory into seven functionally distinct layers — from working memory for active tasks, through episodic memory for concrete experiences, to cross-context memory for connections that span layers. Each layer is addressable through a unified API; a MemoryCoordinator orchestrates storing, retrieving, consolidating, and forgetting.
Fifteen algorithms — each with a peer-reviewed neuroscience source — provide the mechanics: FSRS for directed spaced repetition, Hebbian learning for co-activation, the Ebbinghaus forgetting curve, Bayesian confidence propagation, a two-factor synaptic model, a vmPFC-coupled prediction-error update, and nine more. Sleep consolidation simulates offline memory replay.
Empirical result
From the open-access preprint (arXiv 2604.23878): LongMemEval-500 benchmark, three independent LLM judges (Sonnet 4.5, Opus 4.7, GPT-4o).
Core components
Working, Short-Term, Episodic, Semantic, Procedural, Core, and Cross-Context Memory — each with its own time scale, persistence, and consolidation logic.
A unified API across all seven layers: store(), recall(), consolidate(), decay(), reviewQueue(). Routing follows salience and context automatically.
Free Spaced Repetition Scheduler — vmPFC-coupled variant with prediction-error update. Facts are reviewed at optimal moments, not on a fixed grid.
Offline memory replay as a methodological core. Selection of important memories for reinforcement; directed pruning of weak associations.
Consolidation is driven by two factors — co-activation frequency and a neuromodulator signal. Reproduces experimental findings from the synaptic-tagging literature.
Six components — NeuromodulatorEngine, ReconsolidationEngine, TripleCopyMemory, PriorityMap, StabilityProtector, MetacognitiveMonitor — model predictive memory formation with bias detection.
Get involved
Enquiries from universities, research consortia or funding programmes — on replication studies, consortium proposals or methodological discussion — are explicitly welcome.
research@zensation.aiMore from this research
Core and application fields, on a shared ethics foundation.