🤖 AI Summary
Current large language models implicitly encode experience within fixed parameters, limiting their capacity for persistent memory, temporal grounding, source traceability, and interpretability. To address these shortcomings, this work proposes the Dynamic Graph-based Memory Model (DGMM), which explicitly represents time-evolving episodic-semantic memory using a graph structure and incorporates a cue-conditioned recall mechanism to construct working memory. By treating memory as a first-class structural substrate for reasoning, the architecture enables continual evolution without retraining through additive memory growth and recall conditioned on contextual cues. The model supports episodic persistence, context-adaptive behavior, and localized surprise detection, thereby laying the foundation for building AI systems that are interpretable, temporally grounded, and context-aware.
📝 Abstract
Contemporary artificial intelligence systems achieve strong performance through large-scale parameterization, retrieval augmentation, and training on extensive static corpora. Despite these advances, they continue to face limitations in persistent memory, temporal grounding, provenance, and interpretability. These challenges are especially pronounced in large language models, where experience is encoded implicitly in fixed parameters, limiting the ability to preserve, inspect, and reinterpret past interactions over time.
This paper establishes a memory-centric architectural foundation for artificial intelligence in which experience is represented explicitly and persistently to support temporal grounding, provenance, and interpretability. It proposes an alternative to parameter-centric approaches by treating memory as a first-class, structured substrate for reasoning.
We introduce the Dynamic Gist-Based Memory Model (DGMM), an architecture in which experience is represented as an evolving, graph-structured episodic-semantic memory. DGMM encodes experience as interconnected conceptual structures grounded in time, source, and interaction context, and defines selective, cue-conditioned recall as the mechanism for constructing working memory. A formal schema and architectural invariants are provided based on additive memory growth and recall-conditioned interpretation.
The results specify properties of DGMM, including episodic persistence, locality of cue-conditioned surprise, and contextual variability without structural modification of stored memory. DGMM provides a coherent architectural theory in which memory is explicit and persistent, supporting evolving interpretation without retraining and enabling interpretable, context-aware, and temporally grounded AI systems.