Author

Jun Sakuma

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Preprint Sep 2026

Where Do LLMs Decide to Break the Rules? Mechanistic Localization of Prompt Injection Compliance

When a prompt injection attack succeeds, a Large Language Model (LLM) abandons its assigned system role to comply with an adversarial instruction. While prior work has extensively quantified how often this occurs, we ask a more fundamental question: where inside the network does the model actually decide to break the r...

Rui Wen, Jia-Yang Liu, Ze-Yu Yang et al. · 0 citations

#machine learning Preprint Sep 2026

No Free Efficiency: Revisiting the Trade-off Between Training Efficiency and Model Vulnerability

Training efficiency has become the central driver of recent progress in foundation models. To overcome the massive computational and data requirements of large-scale training, researchers increasingly adopt strategies such as selective data sampling, efficient pre-training, and simplified reinforcement learning pipelin...

Yi-Yong Liu, Jun Sakuma, Michael Backes et al. · 0 citations

Conference Open access Sep 2026

Learning Local Feature Masks with Variational Information Bottleneck

Instance-wise feature selection (IWFS) identifies informative features for each instance, improving generalization by discarding irrelevant information and enhancing interpretability through personalized explanations. Most IWFS methods adopt a selector--predictor architecture, where a selector generates instance-specif...

Lu Sun, Jun Sakuma · Proceedings of the Thirty-Fi... · 0 citations