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Dream Cycle: A Cognitive Consolidation Primitive
Theory-only, implementation-agnostic
Author: Kevin T. Nguyen
Contact: jkdkr2439@gmail.com
What this is
This repository presents Dream Cycle, a standalone cognitive consolidation primitive designed for proto-digital thinking systems.
Dream Cycle defines an offline stabilization loop for systems that accumulate internal state over time and risk degradation due to conflict, overload, or drift.
This work is published in a theory-building stage.
What this is NOT
- Not a trained model
- Not an algorithm implementation
- Not a neuroscience or consciousness claim
- Not a performance benchmark
No empirical validation is reported.
Core idea
Any system that:
- maintains internal state
- operates across long horizons
- accumulates memory, beliefs, or policies
will eventually require a mechanism to:
- selectively consolidate
- safely de-correlate rigid patterns
- preserve identity continuity
- discard non-viable structures
- reset internal pressure
Dream Cycle provides a minimal, architecture-independent specification for that mechanism.
Contents
/paper/— Full LaTeX theory paper (principles + algorithmic skeleton)/system_prompt/— A clean system-prompt version usable for custom AI agents
How to use (no code required)
You can adopt Dream Cycle by:
- Defining a pressure proxy (conflict, entropy, overload)
- Selecting unstable internal residues
- Running an offline consolidation loop following the five stages
- Reinforcing only viable structures
- Resetting pressure before returning to online operation
No specific data structure, model type, or training method is assumed.
Intended audience
- AI agent builders
- Cognitive architecture researchers
- Alignment / stability researchers
- Systems thinkers working on long-horizon behavior
License
MIT License.
Free to use, adapt, and extend.
Attribution appreciated: “Dream Cycle by Kevin T. Nguyen”
Citation
@misc{nguyen2026_dream_cycle_primitive,
title = {Dream Cycle: A Cognitive Consolidation Primitive for
Proto-Digital Thinking Systems},
author = {Nguyen, Kevin T.},
year = {2026},
howpublished = {Hugging Face repository},
note = {Theory-only, implementation-agnostic, no empirical validation}
}
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