arXiv:2605.09504v3 Announce Type: replace-cross
Abstract: We show that the instruments used to measure AI offensive capability fail in two ways: (i) they overstate harm, and (ii) they credit the mode...
By Michael Alexander Riegler, Finn Schwall, Annika Willoch Olstad, Birk Sebastian Frostelid Torpmann-Hagen, Sushant Gautam, Klas H. Pettersen, Inga Str\"umke
arXiv:2606. 26057v1 Announce Type: cross Abstract: AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems.
By Seth Dobrin, {\L}ukasz Chmiel
The paper introduces FUSE, a modular framework that evaluates large language models (LLMs) for dangerous capabilities across three orthogonal pipelines: Knowledge (K), Defense (D), and Harm (H). Using a chemical‑biological module, the authors assess 12 commercial LLMs, revealing divergent profiles among models and families, and showing that newer models increase knowledge while only partially improving defense. The framework’s reliability is supported by high cross‑judge consistency and low inter‑pipeline correlations.
By Zhengyi Jin, Ru Zhang, Xiao Chen, Xinbo Liu, Jiaxuan Lin, Jia Huang, Jianyi Liu, Zhen Yang
Safin-1.5 is a large language model that incorporates a safety‑native architecture and a recursive safety improvement loop to enhance its own safety while maintaining utility. The architecture uses persistent safety states and hazardous‑knowledge disentanglement to isolate and remove risky knowledge with minimal impact on general capabilities. An inference‑time harness extends the model’s abilities to formal reasoning by combining language understanding with symbolic inference and verification, enabling the construction of checkable proofs.
By Jingnan Zheng, Cheng Jin, Ming Zhang, Huan Sun, Zhongtian Ma, Kaisen Yang, Ziyuan Zhou, Tong Wu, Dongcheng Zhang, Yi Zhang, Xiao Wang, Han Qi, Shu Yu, Ermo Hua, Zongji Yu, Chaozhuo Li, Xiaoyu Wen, Zhekai Chen, Jiawei Zhou, Sirui Chen, Ning Ding, An Zhang, Tat-Seng Chua, Xiangnan He, Xia Hu, Bowen Zhou, Qiaosheng Zhang, Youbang Sun, Xiang Wang, Zhongyu Huang, Chaochao Lu
arXiv:2608. 16402v1 Announce Type: new Abstract: Large language model-based agentic frameworks primarily optimize capability: whether an agent can reason, retrieve information, call tools, delegate work, and complete a goal.
By Bhaskar Tripathi, Anurag Kumar, Ramendra Kumar, Bhavesh Gadhe
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