arXiv:2607. 07184v1 Announce Type: cross Abstract: Pre-deployment safety evaluations aim to inform the downstream risks of releasing a new AI model.
By Marcus Williams, Hannah Sheahan, Cameron Raymond, Tomek Korbak, Deng Pan, Peilin Yang, Leon Maksin, Ningyi Xie, Phillip Guo, Ian Kivlichan, Micah Carroll
arXiv:2608. 06202v1 Announce Type: cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness.
By Ro Encarnaci\'on, Tina Behzad, Emma Lurie, Dana\'e Metaxa
arXiv:2607. 14285v1 Announce Type: cross Abstract: Safety alignment in LLMs aims to align models with human values, but which values take precedence when they conflict?
By Aryan Keluskar, Amrita Bhattacharjee, Huan Liu
arXiv:2605. 28591v2 Announce Type: replace-cross Abstract: The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings.
By Katharina Deckenbach, Haritz Puerto, Jonas Geiping, Sahar Abdelnabi
arXiv:2607. 14570v1 Announce Type: new Abstract: AI software development agents are increasingly capable of modifying infrastructure and security critical systems, creating risks where an agent completes its assigned task while covertly weakening safeguards through actions such as broadening permissions, degrading logging, or introducing persistence mechanisms.
By Preeti Ravindra, Rahul Tiwari, Vincent Wolowski
arXiv:2606. 19380v3 Announce Type: replace-cross Abstract: Software engineering and deployment are increasingly delegated to AI coding agents.
By Kenneth Ge, Andre Assis