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cs.AI2026
GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic
Tianyuan Zhang, Peng Yue, Zihao Peng +8
Multimodal large language models (MLLMs) are increasingly integrated into autonomous driving (AD) systems; however, they remain vulnerable to diverse safety threats, particularly i…
cs.AI2026
How Emotion Shapes the Behavior of LLMs and Agents: A Mechanistic Study
Moran Sun, Tianlin Li, Yuwei Zheng +4
Emotion plays an important role in human cognition and performance. Motivated by this, we investigate whether analogous emotional signals can shape the behavior of large language m…
cs.AI2024
Compromising Embodied Agents with Contextual Backdoor Attacks
Aishan Liu, Yuguang Zhou, Xianglong Liu +9
Large language models (LLMs) have transformed the development of embodied intelligence. By providing a few contextual demonstrations, developers can utilize the extensive internal…