LLM-Based Telemetry Repair and Fault Detection in V2X Networks with Digital Twin Guidance
Why this work is in the frame
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Bibliographic record
Abstract
In vehicle-to-everything (V2X) networks, real-time telemetry is essential for enabling predictive analytics and fault detection in intelligent transportation systems. However, frequent wireless disruptions due to interference, mobility, and congestion lead to telemetry gaps that degrade downstream decision-making. To address this challenge, we propose a framework that enhances wireless telemetry robustness using large language models (LLMs) guided by digital twin-based context. Our system combines retrieval-augmented generation with environmental priors to recover high-dimensional, time-correlated telemetry streams lost during communication outages. We also integrate federated continual learning to maintain fault classification performance across non-i.i.d. V2X conditions without centralized data exchange. Extensive evaluations on real-world driving datasets with simulated wireless impairments show that our method significantly improves reconstruction fidelity, reduces degradation from multi-step gaps, and sustains long-term classifier stability. This work demonstrates how AI-driven semantic recovery mechanisms can improve the functional reliability of wireless V2X telemetry under dynamic and lossy network conditions.
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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 | 0.002 |
| 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