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cs.CL2026
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs
Zhenyu Liu, Xuanyu Zhang, Yunxin Li +10
Developing seamless, high-performance, native intelligent full-duplex Spoken Language Models (SLMs) remains a critical challenge and long-standing goal for the speech and NLP commu…
cs.CV2026
CodeShrink: Adaptive Visual Compression for Efficient Multimodal Code Understanding
Wenxin Tang, Jingyu Xiao, Zhenyu Liu +6
Rendering source code as images offers a promising way to reduce the input costs of Multimodal Large Language Models (MLLMs). Adjusting image resolution can trade visual token cost…
cs.IT2026
A Theoretical Interpretation of In-Context Learning via Probabilistic Modeling
Zhenyu Liu, Huaze Tang, Shao-Lun Huang
In-context learning (ICL) is an emerging paradigm that employs the semantic information inherent in large language models (LLMs) for generating answers to user queries. While the r…