arXiv:2606. 17478v1 Announce Type: cross Abstract: As LLMs acquire stronger reasoning capabilities, deceptive behavior becomes an increasingly serious safety concern.
By Kexin Chen, Yi Liu, Haonan Zhang, Yanhui Li, Xinyu Deng, Dongxia Wang
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:2608.23264v1 Announce Type: new
Abstract: Although Large Language Models (LLMs) are aligned to optimize for both helpfulness and harmlessness, these dual objectives may conflict, inevitably lea...
By Or Biton, Tomer Krichli, Itai Allouche, Joseph Keshet
arXiv:2609.36254v1 Announce Type: new
Abstract: Large Reasoning Models (LRMs) are commonly trained with reinforcement learning (RL) to improve their generation of chain-of-thought (CoT) reasoning bef...
By Xiangyu Zhou, Saleh Zare Zade, Rafi Ibn Sultan, Alexander Kotov, Dongxiao Zhu
arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.
By Ali Asad, Stephen Obadinma, Anshul Pattoo, Wenxuan Zhang, Xiaodan Zhu
arXiv:2609.24801v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation throu...
By Fernando Outeda, Gustavo Betarte, Juan Diego Campo, Fiorella Cravero