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cs.LG2025
Monitor and Recover: A Paradigm for Future Research on Distribution Shift in Learning-Enabled Cyber-Physical Systems
Vivian Lin, Insup Lee
With the known vulnerability of neural networks to distribution shift, maintaining reliability in learning-enabled cyber-physical systems poses a salient challenge. In response, ma…
cs.LG2025
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios
Vivian Lin, Ramneet Kaur, Yahan Yang +6
The safety of learning-enabled cyber-physical systems is compromised by the well-known vulnerabilities of deep neural networks to out-of-distribution (OOD) inputs. Existing literat…
cs.CL2025
MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety
Yahan Yang, Soham Dan, Shuo Li +2
Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking, which can elicit harmful or unsafe behaviors. This vulnerability is exacerbated in multili…