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cs.CV2026
SparseSAM: Structured Sparsification of Activations in Segment Anything Models
Hoai-Chau Tran, Chi H. Nguyen, Duy M. H. Nguyen +3
The Segment Anything Model (SAM) achieves strong open-vocabulary segmentation, but its ViT-based image encoders dominate inference latency and memory. Existing activation compressi…
cs.LG2026
tLoRA: Efficient Multi-LoRA Training with Elastic Shared Super-Models
Kevin Li, Dibyadeep Saha, Avni Kanodia +1
As Low-Rank Adaptation (LoRA) becomes the standard approach for efficiently fine-tuning large language models (LLMs), shared clusters increasingly execute many concurrent LoRA trai…
cs.DC2026
PackInfer: Compute- and I/O-Efficient Attention for Batched LLM Inference
Rui Ning, Wei Zhang, Fan Lai
Attention efficiency is critical to large language model (LLM) inference. While prior advances optimize attention execution for individual requests (e.g., FlashAttention), producti…