arXiv:2608. 16564v1 Announce Type: new Abstract: Machine learning (ML) is a key technology driving innovation today, but ensuring ML safety remains a major challenge for safety-related applications.
By Benjamin Herd, Jessica Kelly, Mario Trapp
arXiv:2604. 23099v2 Announce Type: replace-cross Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models and benchmarks.
By Yizheng Huang, Wenjun Zeng, Aditi Kumaresan, Zi Wang
arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.
By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee
arXiv:2410. 22526v2 Announce Type: replace Abstract: To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards.
By Shalaleh Rismani, Roel Dobbe, AJung Moon
arXiv:2608. 13793v1 Announce Type: cross Abstract: Machine learning (ML) has become an indispensable part of modern engineering design workflows.
By Tyler R. Johnson, Kian Ben-Jacob, Christopher P. Muller, Ramin Bostanabad
arXiv:2607. 23134v1 Announce Type: new Abstract: Discovering rare safety-critical failures in autonomous and cyber-physical systems is a fundamental challenge in verification and validation.
By Tanmay Khandait, Preetom Biswas, Hideki Okamoto, Bardh Hoxha, Georgios Fainekos, Giulia Pedrielli