DREAM v3 — Dynamic Retention Episodic Architecture for Memory: Governed Memory through the Separation of Retention and Influence

Version 3.0 of DREAM — Dynamic Retention Episodic Architecture for Memory, advances the architecture from adaptive memory retention toward explicit governance of memory influence.

Previous versions established DREAM as a scalable, opt-in episodic memory architecture for AI agents, introducing adaptive retention, empirical simulations, modular reference implementations, and the DREAM-as-a-Support (DaaS) model. DREAM v3 addresses a deeper problem that emerges when persistent memory begins to shape future reasoning: retaining a memory does not necessarily mean that the memory should remain authorized to influence the current cognitive context.

The central contribution of DREAM v3 is the explicit separation between memory retention and memory influence. A retention policy determines whether a memory should remain available, be archived, consolidated, or eventually pruned. A separate, query-conditioned influence policy determines whether that memory may affect the current reasoning context and with what degree of authority. Retrieved memories are therefore treated as candidates rather than automatically authorized context.

DREAM v3 introduces auditable meta-memory, provenance anchors, compression-lineage tracking, contradiction handling, epistemic recalibration, governed context construction, typed feedback events, and immutable hash-chained decision traces. A state-aware Hygiene Gate evaluates each retrieved memory using confidence, uncertainty, contradiction pressure, source reliability, temporal validity, grounding status, provenance integrity, and query relevance. Depending on its epistemic state, a memory may be authorized, presented as uncertain, assigned low influence, routed to arbitration, quarantined, consolidated, or blocked.

The reference implementation is evaluated using a controlled synthetic benchmark covering stable preferences, changed preferences, corrected facts, repeated errors, conflicting sources, broken compression lineage, and rare but critical memories. Five configurations are compared: no persistent memory, simple retrieval-augmented generation, permanent memory, a DREAM v2-style retention baseline, and the complete DREAM v3 governance pipeline.

Within this controlled evaluation, DREAM v3 achieved 100% grounding fidelity, 0% false reinforcement, 0% continuity loss, and 100% quarantine recall. A component ablation study comprising 7,000 generated configuration runs found that removing the separation between retention and influence produced the largest measured degradation, reaching approximately 34.3% false reinforcement. Removing the Hygiene Gate, provenance-lineage protection, or epistemic recalibration also produced measurable degradation.

These results constitute architecture-level evidence within a controlled synthetic environment. They do not establish generalization to natural conversations, arbitrary language models, multilingual settings, adversarial workloads, or production-scale deployments.

The release includes the research paper, architecture specification, diagrams, Architecture Decision Records, JSON Schema contracts, DREAM v2-to-v3 migration fixtures, deterministic Python reference implementation, synthetic benchmark, component ablation study, machine-readable results, 46 unit and integration tests, and a local interactive governance playground for inspecting memory retrieval, Influence Scores, Hygiene Gate decisions, governed context, and audit traces in real time.

DREAM v3 is presented as a research architecture and executable reference prototype for studying epistemic stability in persistent AI memory. It is not presented as a production-ready memory service, universal truth-determination mechanism, complete cognitive architecture, consciousness model, or neural-weight modification system.

Source code, tests, examples, and benchmark scripts are licensed under the Apache License 2.0. The paper, documentation, diagrams, specifications, and synthetic dataset are licensed under Creative Commons Attribution 4.0 International.

Patrick Morrison is acknowledged for conceptual discussions that helped sharpen the ideas of dynamic equilibrium, memory persistence versus memory influence, meta-memory, cognitive hygiene, contradiction pressure, and epistemic stability. His restrictively licensed Athena / Destiny prototype is not included in this release and was not used as the DREAM v3 codebase.

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.