19 Degrees of Freedom Beat Full-Parameter Fine-Tuning in Fixed-Backbone Antibody Design

Inverse-folding models generate antibody CDR sequences compatible with antibody–antigen structures, but are trained for structural compatibility rather than binding. We introduce PACT (Position-Additive Composition Tilt), which steers a frozen decoder with a shared amino-acid bias of only 19 effective degrees of freedom. Its coefficients are estimated in closed form from within-backbone reward differences, requiring no gradient training. Across three antigens, PACT improves mean Rosetta interface reward by 0.32–0.63 and increases shape complementarity on every target. In contrast, advantage regression yields no significant reward gain over the frozen reference, while filtered SFT reduces reward and falls behind PACT by 0.99–1.83 units. To investigate the limited benefit of full-parameter training, we examine how it reweights candidate designs. Across six tested objectives, fine-tuning produces log-likelihood ratios relative to the frozen reference that are nearly uncorrelated with reward differences among candidates for the same backbone. Checkpoint analysis of advantage regression further shows that these candidates retain highly aligned gradients throughout training. The fitted PACT biases also transfer across targets and improve ESM-IF and AntiFold without refitting. Together, these findings support direct steering of residue preferences as an effective and transferable alternative to the tested full-parameter updates.