3 papers
cs.CV2026
Borrowing from anything: A generalizable framework for reference-guided instance editing
Shengxiao Zhou, Chenghua Li, Jianhao Huang +2
Reference-guided instance editing is fundamentally limited by semantic entanglement, where a reference's intrinsic appearance is intertwined with its extrinsic attributes. The key…
cs.CV2026
Where to Refine, When to Stop: Rethinking Redundancy via Latent Discrepancy for Efficient Visual Autoregressive Generation
Changwang Mei, Peisong Wang, Zekun Li +7
Visual Autoregressive (VAR) models deliver high-quality image generation but suffer from significant inference latency at high resolutions. Recent acceleration approaches most rely…
cs.LG2026
IntraSlice: Towards High-Performance Structural Pruning with Block-Intra PCA for LLMs
Meng Li, Peisong Wang, Yuantian Shao +5
Large Language Models (LLMs) achieve strong performance across diverse tasks but face deployment challenges due to their massive size. Structured pruning offers acceleration benefi…