A Federated Domain-Specific Architecture for Safe and Scalable Artificial Intelligence
Bibliographic record
Abstract
The pursuit of monolithic, general-purpose Artificial General Intelligence (AGI) has led to models that are computationally inefficient, inherently unsafe, and prone to unreliable performance on specialized tasks. We propose a new architectural paradigm, the SyberCraft Architecture, which moves beyond generalization in favor of a "Federation of Specialists." This architecture is a distributed, multi-agent system comprising 147 specialized Large Language Models, each demonstrating mastery over a specific domain. The federation is governed by a dedicated, hierarchical AI C-Suite, operating under a system of internal checks and balances, to ensure strategic alignment, ethical compliance, and meta-cognitive optimization. Communication and coordination are facilitated by Runa, a new, open-standard language designed for unambiguous AI-to-AI interaction. We argue that this federated model provides a more robust, efficient, and provably safer path toward scalable, advanced artificial intelligence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".