arXiv Machine Learning By Christie Djidjev, Nicholas Kaminski

Explainable Runtime Dependency Tracking for AI-RAN Conflict Monitoring

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arXiv:2606. 06663v1 Announce Type: new Abstract: Future AI-integrated Radio Access Networks (AI-RAN) will combine open programmability with learning-enabled xApps, rApps, and control functions that act on shared parameters and key performance indicators (KPIs).

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 Machine Learning.

arXiv Machine Learning
Jun 5

Event Detection for Parameter-to-KPI Dependency Learning for AI-RAN

arXiv:2606. 06459v1 Announce Type: new Abstract: Next-generation wireless networks are expected to rely on multiple concurrent AI-driven control functions that optimize different network objectives simultaneously, particularly in AI-integrated and open radio access network architectures such as AI Radio Access Network (AI-RAN) and Open Radio Access Network (O-RAN).

By Christie Djidjev, Nicholas Kaminski
arXiv AI
Aug 28

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.

By Hugo Math