4 papers
REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak
Jiachen Ma, Jiawen Zhang, Xiangtian Li +3
While Large Language Models (LLMs) demonstrate remarkable capabilities, they remain susceptible to sophisticated, multi-step jailbreak attacks that circumvent conventional surface-…
Native Reasoning Models: Training Language Models to Reason on Unverifiable Data
Yuanfu Wang, Zhixuan Liu, Xiangtian Li +2
The prevailing paradigm for training large reasoning models--combining Supervised Fine-Tuning (SFT) with Reinforcement Learning with Verifiable Rewards (RLVR)--is fundamentally con…
TrinityGuard: A Unified Framework for Safeguarding Multi-Agent Systems
Kai Wang, Biaojie Zeng, Zeming Wei +7
With the rapid development of LLM-based multi-agent systems (MAS), their significant safety and security concerns have emerged, which introduce novel risks going beyond single agen…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…