4 papers
LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs
Chacha Chen, Matthew Jörke, Adam GoliÅski +4
Modern AI systems are being deployed in complex domains such as medicine, science, and law, where it is important that they not only produce correct answers, but also represent and…
Hiding in Plain Text: Detecting Concealed Jailbreaks via Activation Disentanglement
Amirhossein Farzam, Majid Behabahani, Mani Malek +2
Large language models (LLMs) remain vulnerable to jailbreak prompts that are fluent and semantically coherent, and therefore difficult to detect with standard heuristics. A particu…
Order-Aware Test-Time Adaptation: Leveraging Temporal Dynamics for Robust Streaming Inference
Young Kyung Kim, Oded Schlesinger, Qiangqiang Wu +2
Test-Time Adaptation (TTA) enables pre-trained models to adjust to distribution shift by learning from unlabeled test-time streams. However, existing methods typically treat these…
Chain-of-Image Generation: Toward Monitorable and Controllable Image Generation
Young Kyung Kim, Oded Schlesinger, Yuzhou Zhao +2
While state-of-the-art image generation models achieve remarkable visual quality, their internal generative processes remain a "black box." This opacity limits human observation an…