arXiv AI August 28, 2026 By Hugo Math

Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences

The paper proposes a new framework for automated fault diagnostics in modern vehicles by treating diagnostic trouble codes (DTCs) as a high‑dimensional language. It introduces Transformer‑based models for predictive maintenance, scalable causal discovery methods, and a multi‑agent system that automatically generates Boolean error‑pattern rules. The approach aims to replace costly manual grouping of DTCs with scalable, data‑driven techniques.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles

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