A May 2026 arXiv preprint (abs/2605.23971) introduces a physics-guided machine-learning pipeline that extracts concentration estimates from resistance transients in mixed-phase SnO-SnO2 films showing temperature-dependent p-n switching. The study uses cycle-level descriptors, FFT and DWT summaries, and leakage-aware grouped cross-validation to train separate Random Forest models for the p-type and n-type regimes. In the p-branch the fused classifier reaches 96.5 percent accuracy; in the n-branch the regressor yields MAE of 1.48 ppm and R-squared of 0.992. Because the work remains a preprint, it has not undergone peer review and the authors supply no explicit sample-size or long-term drift statistics. Traditional metal-oxide CO sensors rely on steady-state resistance alone and suffer from humidity cross-sensitivity and baseline drift; this approach instead encodes transient dynamics that are physically interpretable, addressing a gap noted in a 2022 Sensors and Actuators B review on nanostructured SnO2 devices. A 2024 ACS Sensors paper on p-n heterojunctions similarly showed that switching polarity improves selectivity but lacked quantitative regression benchmarks. The new framework therefore fills that missing quantitative link while preserving interpretability, yet real-world deployment will still require field calibration against interferents such as hydrogen and ethanol. If commercialized, the dual-regime logic could be embedded in low-power edge chips within months, shortening the path from lab transient to actionable household alarm.