Author

Yue Wu

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#natural language process... Review Sep 2026

The Evolution of Attention in Large Language Models: Mechanisms, Trade-offs, and Emerging Trends

Self-attention gives LLMs fine-grained, query-dependent access to context, but dense token interactions incur quadratic prefill cost and a key--value cache growing with context length. Research thus spans explicit-memory compression, sparse access, recurrent state construction, structured state dynamics, and heterogene...

Zhen-Tao Tan, Jing-Yi Shen, Yan-Bo Li et al. · 0 citations

#artificial intelligence Open access Sep 2026

SPIDER: Multi-Layer Semantic Token Pruning and Adaptive Sub-Layer Skipping in Multimodal Large Language Models.

Multimodal Large Language Models face significant efficiency challenges that stem from two distinct yet coupled sources: data redundancy and computational redundancy. While most methods focus on data redundancy by pruning visual tokens from the output of the visual encoder or computing redundancy in LLM decoders using...

Tian-Xiang Chen, Zhentao Tan, Zi Ye et al. · IEEE Transactions on Pattern... · 0 citations

#artificial intelligence Preprint Sep 2026

From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs

As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectures such as Mixture-of-Experts (MoE) models. This shift raises a key question for parameter-efficient fine-tuning (PEFT): at what granularity should parameters be select...

Zhentao Tan, Chang Liu, Yao Liu et al. · 0 citations