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cs.AI2026
SIGMA: Self-Improving Alignment Generalization from a Model Spec
Jingyu Zhang, Shruti Palaskar, Daniel Khashabi +3
LLM agents are increasingly capable of executing complex tasks and of recursively improving themselves on easy-to-verify objectives such as software engineering and mathematics. Si…
cs.AI2026
Visual Grounding Safety in Vision-Language Models
Erfan Shayegani, Kundan Krishna, Yue Dong +3
Vision-language models (VLMs) are increasingly trained to generate structured outputs like points and bounding boxes that downstream interfaces, agents, and robots can act on, yet…
cs.AI2026
Understanding Annotator Safety Policy with Interpretability
Alex Oesterling, Donghao Ren, Yannick Assogba +4
Safety policies define what constitutes safe and unsafe AI outputs, guiding data annotation and model development. However, annotation disagreement is pervasive and can stem from m…