Tug-of-War No More: Harmonizing Accuracy and Robustness in Vision-Language Models via Stability-Aware Task Vector Merging

Foundation Vision-Language Models (VLMs) excel across benchmarks yet remain vulnerable to adversarial attacks. While adversarial fine-tuning improves robustness, attaining a desirable clean–robust performance trade-off typically requires costly hyperparameter searches with multiple retraining runs. A promising alternative is to merge task vectors (i.e., parameter displacements from pre-trained models) to balance accuracy and robustness without retraining. However, we find that naive task-vector merging produces a near-linear trade-off, as it equally weights all coordinates and fails to distinguish weights that aid both objectives from those that create conflicts. To overcome this limitation, we propose a prediction stability-aware merging framework that composes task vectors from off-the-shelf naturally and robustly fine-tuned VLMs. Our key insight is that prediction stability serves as a proxy for cross-objective compatibility, enabling us to favor perturbation-invariant parameters while attenuating those with high cross-objective impact. Specifically, we estimate per-parameter stability from gradients under both objectives, building complementary masks that retain jointly stable coordinates while suppressing counterpart-sensitive ones. We further refine these masks along adversarial parameter trajectories, with steps weighted by a prediction-sensitivity index. Our theoretical analysis shows that the masks provably contract first-order cross-objective interference, and the prediction criticality index tracks curvature, biasing the merge toward flatter minima and better generalization. Extensive experiments across benchmarks and scenarios demonstrate our method consistently achieves superior clean–robust trade-offs over prior approaches, with the learned balance transferring effectively to downstream tasks.