Deep learning has transformed seismic phase picking, but a systematic failure mode persists: for some S-wave arrivals that appear unambiguous to human analysts, the model produces only a distorted peak trapped below the detection threshold, even as the P-wave prediction on the same record appears flawless. By examining training dynamics and loss landscape geometry, we diagnose this amplitude suppression as an optimization trap arising from three interacting factors. Temporal uncertainty in S-wave arrivals, CNN bias toward amplitude boundaries, and the inability of pointwise loss to provide lateral corrective forces combine to create the trap. The diagnosis reveals that phase arrival labels are structured shapes rather than independent probability estimates, requiring training objectives that preserve coherence. We formalize this as the shape-then-align strategy and validate it through a conditional GAN proof of concept, recovering previously sub-threshold signals and achieving a 64% increase in effective S-phase detections. Beyond this implementation, the loss landscape visualization and numerical simulation techniques we introduce provide a general methodology for analyzing how label designs and loss functions interact with temporal uncertainty, transforming these choices from trial-and-error into principled analysis.