DREAM — Dynamic Retention Episodic Architecture for Memory

Version 2.0 of the DREAM architecture extends the original conceptual proposal by introducing empirical validation, architectural refinements, and public reference implementations.

While version 1.0 focused on defining DREAM (Dynamic Retention Episodic Architecture for Memory) as a scalable, opt-in, episodic memory framework for AI agents, this release advances the work toward a validated architectural blueprint. The v2.0 update includes quantitative simulations evaluating memory retention behavior, storage growth, and energy-related cost proxies under adaptive retention policies, demonstrating the practical implications of DREAM’s design choices.

This version also introduces a refined architectural model and updated data flows, alongside a conceptual extension named DREAM-as-a-Support (DaaS). The DaaS model reframes DREAM from a standalone memory system into a foundational platform layer capable of providing memory governance primitives—such as
adaptive retention and user-centric opt-in—as on-demand services to complementary cognitive architectures. This extension validates the original four-pillar design by demonstrating its modularity, scalability, and internal
API-oriented structure.

To support reproducibility and experimentation, this release is accompanied by public research artifacts, including a reference implementation of the DREAM architecture and a modular Python framework designed to operationalize the proposed concepts. These implementations are intended for architectural
validation, simulation, and research use, and do not represent production-ready systems.

Overall, version 2.0 transitions DREAM from a purely theoretical proposal into an empirically grounded, extensible architectural framework, reinforcing its applicability as a long-term memory layer for contemporary and future AI agent systems.

Research Artifacts and Reference Implementations

• DREAM Architecture (design artifacts, diagrams, and simulations):
  DREAM Architecture

• DREAM Framework (modular Python framework implementing the DREAM concepts):
  DREAM Framework

Experimental Artifacts and Simulations

• Energy cost simulation and adaptive retention analysis:
  Energy Cost Simulation

• Memory storage growth simulation and comparative evaluation:
  Storage Cost Simulation

These notebooks provide reproducible simulations supporting the empirical
evaluation presented in this paper and are intended for research and
validation purposes.