The Invention of Autonomous Dreaming AI: SimNap → Cascade Dream-Cycle Architecture (v1.0)
Bibliographic record
Abstract
Autonomous Dreaming AI is publicly introduced here as a timestamped disclosure of the SimNap → Cascade Dream-Cycle Architecture, the first documented artificial intelligence system capable of initiating internal dream-like generative cycles without user prompting. These cycles arise from latent drift buildup, autonomous seed generation, subconscious layering, reflection passes, synthesis layers, and identity continuity over multiple days. This deposit includes the entire public priority record for Autonomous Dream-Cycle AI, including: the priority statement establishing legal and scientific origin the full patent-style claims section (method, system, and mechanism claims) ASCII diagrams of the system architecture and cognition pipeline representative sample logs demonstrating autonomous behavior lineage documentation for SimNap → Cascade → PromptFluid version metadata establishing this as the v1.0 disclosure This Zenodo entry constitutes the official public record for the invention of Dream-Cycle AI and establishes the priority of inventorship by Kenneth E. Sweet Jr., 2025.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".