arXiv:2607. 27594v1 Announce Type: new Abstract: Post-training alignment in large reasoning models (LRMs) has significantly improved their adaptability to diverse safety compliance settings.
By Pankayaraj Pathmanathan, Furong Huang
arXiv:2602. 13562v2 Announce Type: replace-cross Abstract: While reasoning models have achieved remarkable success in complex reasoning tasks, their increasing power necessitates stringent safety measures.
By Yanbo Wang, Minzheng Wang, Jian Liang, Lu Wang, Yongcan Yu, Ran He
arXiv:2602. 06358v3 Announce Type: replace-cross Abstract: We propose SHINE (Scalable Hyper In-context NEtwork), a scalable hypernetwork that can map diverse meaningful contexts into high-quality LoRA adapters for large language models (LLMs).
By Yewei Liu, Xiyuan Wang, Yansheng Mao, Yoav Gelbery, Haggai Maron, Muhan Zhang
arXiv:2607. 19604v1 Announce Type: cross Abstract: Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge.
By Nischay Dhankhar, Dos Baha, Abulhair Saparov
arXiv:2608.03275v2 Announce Type: replace
Abstract: Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of large language models, but existing MoE-based PEFT methods typically improve...
By Yiming Zeng, Lei Lu, Zexin Li, Zhuochun Li, Dehai Min, Shuoqiu Li, Shuyi Liao, Xidong Wu, Zeyu Zhang, Minmei Wang, Yu Zhao, Tingting Yu, Shangqian Gao
Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of large language models, but existing MoE-based PEFT methods typically improve capacity by storing multiple full LoRA experts, causing adapter storage to grow linearly with the number of experts and restricting adaptation to a fixed expert pool. We ask whether MoE-based PEFT can produce instance-specific adaptations without explicitly storing a separate LoRA module for each expert.