Recursive self-improvement (RSI) enables large language models to advance their capabilities through successive updates, but these updates can also introduce new risks and weaken existing safeguards. In line with the AI-45{\deg} Law, we pursue Safety RSI, in which safety develops as an intrinsic capability alongside ge...
Jing-Nan Zheng, Cheng Jin, Ming Zhang et al.· 0 citations
This paper introduces AgentSafeEvo, a co-evolutionary reinforcement learning framework for agent safety built on an attacker-defender game spanning diverse tasks and attack types, and introduces learnability-guided task weighting, which uses within-task defender-reward variability to adjust each task's contribution to...
Chen-Xu Wang, Xiaoyu Wen, Han Qi et al.· 0 citations
Recursive self-improvement, the participation of AI systems in improving their own capabilities, is beginning to move from theoretical prospect to practice, posing both challenges and opportunities for safety alignment. Models evolve through frequent updates, and their safety alignment requires continual adaptation to...
Jing-Nan Zheng, Dong-Cheng Zhang, Yi Zhang et al.· 1 citation
ReCode is introduced, a compositional budget-efficient attack that combines desensitization rewriting with two effective low-cost primitives identified by Fair-ASR, to provide a comparable evaluation basis for black-box jailbreak attacks under shared target-call budgets B.
The routed-state interface unifies contextual memory and persistent capability adaptation within the model's native computation, reframing memory from a passive record of prior context into an active substrate for maintaining and evolving model behavior.
Ming Zhang, Kai-Sen Yang, Shu Yu et al.· 0 citations
This work introduces \textsc{JailbreakSkill}, a skill-centric framework for scaling automated red-teaming through reusable and continuously evolving attack capabilities, which packages existing attack strategies into modular, agent-ready skills that can be directly reused and adaptively selected across tasks and target...
Xiaoyu Wen, Jiajia Li, Zhida He et al.· 2 citations
This work presents SkillSafetyBench, a runnable benchmark for evaluating skill-facing safety failures, and suggests that agent safety depends not only on model-level alignment, but also on how agents interpret skills, trust workflow context, and act through executable environments.
