A Real Time Self-Generating Control for AI Platforms
Bibliographic record
Abstract
Recent advancements in hardware and software implementations of Artificial Intelligence have sparked a multitude of revolutionary applications in the theory and implementation of AI algorithms and tools. Significant new developments have been driven by the application of the Transformer concept in the fields of Large Language Models (LLMs), Reinforcement Learning, and other areas. This in turn led to capturing long-range dependencies and contextual information based on data. More recently, strong positions in the AI research community, around the proper implementations and usage of certain Machine Learning (ML) applications, have been thoroughly debated. However, it is very much known, that ChatGPT and other like platforms, such as Llama, or large GNNs, suffer from a series of black-box drawbacks out of which the “factual accuracy”, “halucinations”, “overgeneralization” and others open loop LLMs were reported in the literature. This paper presents an Autonomic Computing (AC) closed-loop architecture that manages and gathers data from user prompts via a DOMifire module. The DOMifire acts as the sensor element of the LLM AC system, referred to as the Plant. This data is logically compared by an Expert System (ES), which serves as the core of the Autonomic Manager in the AC loop of the LLM, with the data obtained from the LLM's output―in this case, the responses generated by ChatGPT. After a reduced number of iterations, the results are evaluated using a Mean Absolute Scaled Error (MASE) metric. In the context of a time series, this process results in a stable set of sentences or rules produced by the Knowledge Base module. An example, in which the “Time Series” of AutoGluOn illustrates the AC - AI interactions for a complementary contributions to a more robust AI platform. An example of the interaction AC-AI is given in the Conclusion section of this paper.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".