This study investigates how internal phonetic representations in self-supervised speech models behave under fine-grained dialect variation, addressing the limitations of existing probing studies that rely on curated corpora. The authors present a case study using an entirely unlabeled probing pipeline for Mandarin sub-dialects. Phone sequences are generated via a language-agnostic universal phone recognizer and mapped to articulatory feature vectors, enabling frame-level probing without manual annotation. Results reveal structured patterns in articulatory feature decodability across different Mandarin dialects. Acoustically salient features like labiality and stridency remain comparatively stable, while those associated with finer spectral distinctions show larger dialect-dependent variation. This variation is driven primarily by elevated decodability for Beijing speech relative to other sub-dialects. Layer-wise analyses demonstrate distinct representational dynamics for these feature groups, suggesting uneven dialect sensitivity across articulatory dimensions.