11 citations · 11 across the 9 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.LG2026
Stepped MoE: Segment-Level Routing with Configurable Inference Complexity
Arnav Kundu, Zhaoyang Xu, Bairu Hou +3
Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints remains challenging. While el…
cs.DC2026
Parallel Track Transformers: Enabling Fast GPU Inference with Reduced Synchronization
Chong Wang, Nan Du, Tom Gunter +8
Efficient large-scale inference of transformer-based large language models (LLMs) remains a fundamental systems challenge, frequently requiring multi-GPU parallelism to meet string…
cs.CL2026
SPLA: Block Sparse Plus Linear Attention for Long Context Modeling
Bailin Wang, Dan Friedman, Tao Lei +1
Block-wise sparse attention offers significant efficiency gains for long-context modeling, yet existing methods often suffer from low selection fidelity and cumulative contextual l…