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cs.SE2026
Unraveling the Efficacy of Neuron Coverage Criteria on Testing Large Language Models
Xiaoning Ren, Jiayang Song, Chongyang Liu +3
Neuron coverage criteria aim to assess test suite quality and guide test input generation, but their effectiveness for Transformer-based language models remains unclear. We evaluat…
cs.SE2026
Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs
Xiaoning Ren, Yinxing Xue, Lei Ma +1
As Code Large Language Models (LLMs) become central to modern software engineering, their inherent stochasticity poses significant real-world risks, where even minor errors can lea…
cs.RO2026
ROBOSHACKLES: A Safety Dataset for Human-Injury Prevention in Embodied Foundation Models
Zhuowen Yin, Chongyang Liu, Wenzhang Yang +2
Embodied Foundation Models (EFMs) integrate multimodal understanding, future-state reasoning, and executable robot actions. Yet their safety alignment for human-injury prevention r…