Not All Updates Help: Functional Path Attribution for Personality Vector Merging

Personality vector merging enables training-free personality control by adding parameter displacements obtained from personality fine-tuning. However, existing methods typically treat the entire displacement as a coherent personality vector. We show that this assumption overlooks substantial functional cancellation: along parameter-space merging paths, many parameter displacements contribute negatively to the target personality objective, and including them can weaken the resulting personality behavior. We propose Functional Path Attribution, which integrates parameter-wise contributions along personality merging paths and selectively retains the most functionally constructive updates to construct sparse personality vectors. Across Big Five personality control experiments, these vectors achieve stronger target-trait expression than their source fully fine-tuned models while substantially better preserving general capabilities, outperforming random, magnitude-based, and other parameter-selection baselines. These results show that personality vectors are functionally heterogeneous and that path-aware selective merging provides a more effective approach to parameter-space personality control.