RoadPowerFM: Graphormer-JEPA-Based Foundation Model for Road-Power Coupling Network
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
Coupling effects in road-power coupling networks (RPCNs) attract increasing attention, as they are crucial for addressing the road-power intertwined challenges caused by the rapid growth of electric vehicles (EVs). To solve these challenges, this paper, based on Graphormer and Joint-Embedding Predictive Architecture (JEPA), proposes a road power foundation model (RPFM) as a generic solution. Our RPFM, by employing graph-pretraining methods to bridge RPCNs, demonstrates exceptional performance in downstream tasks such as EV Charging Station (EVCS) Load Prediction, Road Traffic Prediction, and EVCS Location Planning. By experimenting on real world dataset, the proposed methodology is proved to be generic and achieves state-of-the-art performance across downstream tasks by improving an average of 7.53% of baseline models’ performance.
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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 it