2 papers
cs.CL2026
Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding
Konstantin Berestizshevsky, Renzo Andri, Lukas Cavigelli
We present Top-Theta (Top-) Attention, a training-free method for sparsifying transformer attention during inference. Our key insight is that static, per-head thresholds can be…
cs.AR2025
Intent-Driven Storage Systems: From Low-Level Tuning to High-Level Understanding
Shai Bergman, Won Wook Song, Lukas Cavigelli +3
Existing storage systems lack visibility into workload intent, limiting their ability to adapt to the semantics of modern, large-scale data-intensive applications. This disconnect…