NATIVE UPDATE ORIENTATION IN FROZEN PROTEIN STRUCTURE PREDICTORS

We study whether adapter parameterization changes the effect of output-channel rotation under a fixed training budget. Our Factor head composes residue-level increments through a frozen predictor’s native pair-generating operators. The generic head G+ explicitly receives the interface anchors and has a free output layer, making its function class closed under output rotation. Our four-cell design trains Native and Rotated versions of each head separately from the same zero- residual function under paired settings. At identical inputs, interface anchors and parameters, orthogonal rotations preserve local writer spectra. The interaction Ψ is the difference between the two heads’ signed Native–Rotated score differences, rather than their absolute responses. On 192 prospectively locked new targets, fixed Protenix/ESMC models establish a Cα pair-lDDT interaction of +0.02515 [+0.01832, +0.03213]; OpenFold’s Fresh96 primary interaction remains unestab- lished. In an observed-panel follow-up, the Protenix interaction remains positive after a prespecified doubling of the update budget, while Factor’s own Native– Rotated score difference decreases. An amended OpenFold schedule study on observed targets and one fixed rotation finds the largest interaction at the lowest absolute quality. We then test whether a shared trainable orthogonal output map gives rotated Factor a larger compensation gain than Native Factor. Rotated Factor improves in both executions, but this gain difference was not re-established in the same-seed repeat. A prespecified reconstruction diagnostic did not establish the predicted ranking of post-training Native–Rotated score differences across eight tested rotations. Together, these findings motivate reporting absolute performance, signed within-head score differences and their interaction under explicit training budgets.