Joint Affine Spectral Shaping: Coupling Weight and Bias Updates Beyond Weight-Only Muon

Matrix spectral optimizers reshape weight-update spectra but usually delegate vector-valued biases to a separate optimizer. We study whether this separation is neutral. We formulate each affine layer as a joint momentum matrix A=[M_W,αm_b] and apply a capped regularized-inverse spectral map to the complete matrix, producing both the weight and physical bias updates. A strict five-seed ablation on a four-layer BERT-mini trained from scratch on IMDb compares exact-SVD Muon, weight-only inverse shaping, affine-probe inverse shaping, and the proposed joint regularized inverse (JRI). Weight-only inverse shaping raises validation-loss-selected test accuracy from 84.903\pm0.242% to 85.562\pm0.308% and lowers selected test loss from 0.3479 to 0.3345. Allowing bias to alter the joint SVD while retaining an independent Adam bias update does not improve over weight-only inverse shaping. Using the transformed bias jointly raises selected test accuracy to 85.738\pm0.180% and lowers test loss to 0.3291, with all five seeds improving relative to the probe baseline. During the peak-performance window, JRI preserves the eligible weight-update norm while reducing the bias-update norm from 0.02095 to 0.00301, lowers boundary-function share from 86.58% to 78.97%, and changes the cosine between weight-induced boundary motion and explicit bias from +0.030 to -0.137. An independent 22-seed replication yields 85.743\pm0.203% selected test accuracy. These results identify joint affine spectral allocation as a small but consistent extension to weight-only spectral optimization.