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This paper presents a tutorial on using Joint-Embedding Predictive Architecture (JEPA) for self-supervised learning in 6G networks, along with a beam management case study and open challenges.
This paper introduces PROBE, a multi-stage pipeline for diagnosing 802.11 packet captures that combines deterministic normalization, multi-run ensemble, and a verdict-aware evidence framework to produce reliable and calibrated diagnoses, outperforming single-pass LLM analysis and naive ensemble voting.