arXiv:2606.07334v5 Announce Type: replace Abstract: This report treats chord-symbol sequences as an interpretable, controllable time series for genre-local harmonic modeling. The frozen Music Transformer base is weight-identical to a pop-dominated Phase-0 baseline. I adapt it to eleven target genres: blues, bossa nova, Bach chorales, country, electronic, folk, funk, gospel, hip-hop, R&B/soul, and rock. The main grid compares LoRA, IA3, BitFit, prefix tuning, and full fine-tuning over 11 genres and 3 seeds (165 cells). All five methods improve over the frozen base on held-out chord prediction (macro gains +2.89 to +3.61 percentage points). Top-1 also scores the three header tokens of each sequence, which move whole runs: only a matched adapter predicts its genre marker, and whether a run predicts the placeholder C-major key of every Chordonomicon song is close to arbitrary. Without the header tokens the macro gains are +1.64 to +2.38 pp, and outside Bach chorales LoRA, IA3, prefix tuning, and full fine-tuning give the same gain (+1.30 to +1.34 pp). That gain reflects a handicap of the frozen base, which for every target but rock reads a genre marker it never read in training: given the pop marker instead, it comes within 0.12 pp of the released matched adapter in every non-chorale genre. Wrong-genre adapters beat the frozen base only where it reads such a marker, and outside Bach chorales the matched adapter's edge over the best wrong one is within 0.2 pp. Only on Bach chorales does adaptation add clearly more (+9.6 pp for LoRA over the base given the pop marker). Further diagnostics (rank sweeps, matched-size and same-weights controls, chord-only classification, real songs) support a bounded conclusion: for genres the base already covers, chord-symbol adaptation mainly supplies a usable genre marker, and chord symbols alone do not carry complete genre identity. Perceived genre authenticity is left to controlled listener evaluation.