2 papers
cs.LG2026
The Geometry of Alignment Collapse: When Fine-Tuning Breaks Safety
Max Springer, Chung Peng Lee, Blossom Metevier +5
Fine-tuning aligned language models on benign tasks unpredictably degrades safety guardrails, even when training data contains no harmful content and developers have no adversarial…
cs.LG2025
SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification
Sasan Tavakkol, Lin Chen, Max Springer +4
Scarcity of labeled data, especially for rare events, hinders training effective machine learning models. This paper proposes SYNAPSE-G (Synthetic Augmentation for Positive Samplin…