Retraction Notice: Integrating Large Language Models and Metaverse in Autonomous Racing: An Education-Oriented Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This letter is the third report from a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on intelligent vehicles for education (IV4E). Autonomous racing serves as a vital platform for nurturing engineering talents among university students, contributing to the development of skills essential for the intelligent vehicle industry. This letter investigates how recent emerging techniques, such as large language models (LLMs) and the Metaverse, can contribute to organizing IV4E-oriented autonomous racing events. Among these DHWs, scholars from diverse fields have collectively explored the integration of LLMs and the Metaverse into autonomous racing for educational purposes. The discussions emphasize the role of Metaverse in creating dynamic and immersive training virtual reality platforms and the role of LLMs in enhancing race commentary and the spectator experience. Within this context, the Metaverse introduces complex scenarios to the racetrack, maintaining suspense about the winning team until a race's final moment. This dynamic feature excites the race and motivates the participating teams to intensify their competition efforts. LLMs facilitate personalized commentary, inspiring spectators to become future participants in these races. Our DHWs highlighted a future in which technology, autonomy, and education intersect, fostering inclusive, educational, and engaging autonomous racing events.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Research integrity Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it