5 papers
vAttention: Verified Sparse Attention
Aditya Desai, Kumar Krishna Agrawal, Shuo Yang +5
State-of-the-art sparse attention methods for reducing decoding latency fall into two main categories: approximate top- (and its extension, top-) and recently introduced samp…
Inference Time Context Sparsity: Illusion or Opportunity?
Sahil Joshi, Prithvi Dixit, Agniva Chowdhury +5
Sparsity has long been a central theme in LLM efficiency, but its role in context processing remains unresolved. As LLM workloads shift toward longer contexts and agentic interacti…
RedunCut: Measurement-Driven Sampling and Accuracy Performance Modeling for Low-Cost Live Video Analytics
Gur-Eyal Sela, Kumar Krishna Agrawal, Bharathan Balaji +2
Live video analytics (LVA) runs continuously across massive camera fleets, but inference cost with modern vision models remains high. To address this, dynamic model size selection…
Pillar-0: A New Frontier for Radiology Foundation Models
Kumar Krishna Agrawal, Longchao Liu, Long Lian +11
Radiology plays an integral role in modern medicine, yet rising imaging volumes have far outpaced workforce growth. Foundation models offer a path toward assisting with the full sp…
Atlas: Multi-Scale Attention Improves Long Context Image Modeling
Kumar Krishna Agrawal, Long Lian, Longchao Liu +6
Efficiently modeling massive images is a long-standing challenge in machine learning. To this end, we introduce Multi-Scale Attention (MSA). MSA relies on two key ideas, (i) multi-…