6 papers
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
Chuanming Yu, Jiaming Liu, Zihao Ge +4
Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches. At th…
Is Measurement Enough? Rethinking Output Validation in Quantum Program Testing
Jiaming Ye, Xiongfei Wu, Shangzhou Xia +2
As quantum computing continues to emerge, ensuring the quality of quantum programs has become increasingly critical. Quantum program testing has emerged as a prominent research are…
S2ST-Omni: Hierarchical Language-Aware SpeechLLM Adaptation for Multilingual Speech-to-Speech Translation
Yu Pan, Xiongfei Wu, Yuguang Yang +3
Despite recent advances in speech-to-speech translation (S2ST), it remains difficult to achieve both high translation accuracy and practical flexibility. In this paper, we present…
SHARP: Synthesizing High-quality Aligned Reasoning Problems for Large Reasoning Models Reinforcement Learning
Xiong Jun Wu, Zhenduo Zhang, ZuJie Wen +11
Training large reasoning models (LRMs) with reinforcement learning in STEM domains is hindered by the scarcity of high-quality, diverse, and verifiable problem sets. Existing synth…
The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Models
Linghan Huang, Haolin Jin, Zhaoge Bi +6
Large language models (LLMs) have seen widespread applications across various domains, yet remain vulnerable to adversarial prompt injections. While most existing research on jailb…
Foundation Models for Autonomous Driving System: An Initial Roadmap
Xiongfei Wu, Mingfei Cheng, Xiaoning Ren +8
Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous dr…