AIM: Model Modulation with Logits Redistribution

The problem

Serving different stakeholder needs by maintaining separately trained models can be costly. AIM studies changes to model behaviour without retraining or access to the original training data.

The method

AIM redistributes logits to support utility modulation and focus modulation. The former adjusts output utility; the latter shifts attention toward selected input features. These are behaviour-control mechanisms, not simply an authorisation switch.

Evidence and scope

The implementation covers image classification, semantic segmentation and text generation. The relevant evidence is how modulation changes these tasks under the evaluated settings, not a claim that any downstream policy can be enforced through logits alone.

See the original code and paper above. CoreLocker and AdaLoc address complementary model-access questions.

@inproceedings{wang2025ai,
  author = {Wang, Zihan and Ma, Zhongkui and Feng, Xinguo and Mei, Zhiyang and Ma, Ethan and Wang, Derui and Xue, Minhui and Bai, Guangdong},
  title = {{AI} Model Modulation with Logits Redistribution},
  booktitle = {Proceedings of the ACM Web Conference 2025},
  year = {2025},
  url = {https://openreview.net/forum?id=lOSomJvrc5}
}