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cs.CR2026
Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners
Leo Y. Lin, Mikhail Kuznetsov, Muslum Ozgur Ozmen +1
Embodied vision-language models (VLMs) are increasingly deployed as high-level planners for robots because they generalize across diverse environments. However, this requires their…
cs.CR2026
A Self-Explainable Deep Architecture for Security Applications
Ananth Shreekumar, Jyun-Jhu Syu, Muslum Ozgur Ozmen +2
Deep learning models have become integral to security applications due to their ability to model complex relationships in data and detect sophisticated threats. However, their comp…
cs.CR2026
RoboJailBench: Benchmarking Adversarial Attacks and Defenses in Embodied Robotic Agents
Doguhuan Yeke, Yanming Zhou, Leo Y. Lin +3
Recent advances in Vision-Language Models (VLMs) facilitate a new class of embodied AI systems, where these models are integrated into physical platforms, e.g. robots and autonomou…