DGMM: A Memory-Centric Architecture for AI
The Dynamic Gist-Based Memory Model (DGMM) proposes a memory-centric alternative to parameter-centric AI architectures. Current systems, including large language models, rely on large-scale parameterization and static corpora, leading to limitations in persistent memory, temporal grounding, provenance, and interpretability. DGMM treats memory as a first-class, structured substrate for reasoning, enabling explicit and persistent representation of experience. This approach aims to preserve, inspect, and reinterpret past interactions over time, addressing key challenges in contemporary AI. The paper introduces DGMM as a foundational architecture for artificial intelligence.
Key facts
- DGMM stands for Dynamic Gist-Based Memory Model.
- The paper proposes a memory-centric architectural foundation for AI.
- Current AI systems face limitations in persistent memory, temporal grounding, provenance, and interpretability.
- Large language models encode experience implicitly in fixed parameters.
- DGMM treats memory as a first-class, structured substrate for reasoning.
- The architecture supports explicit and persistent representation of experience.
- The paper is available on arXiv with ID 2605.02106.
Entities
Institutions
- arXiv