activity
20242026
most citedCAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion Models

1 citations · 1 across the 5 of their papers we have counts for

collaborators

9 papers

quant-ph2026

QALM: Escaping Local Minima via Interleaved Exploration and Exploitation in Quantum Circuit Optimization

Aidan Wagner, Mingkuan Xu, Pengyu Liu +2

Quantum circuit optimizers face a fundamental limitation in how they tolerate temporary cost increases. At one extreme, greedy rule-based optimizers immediately apply any cost-redu…

cs.AI2026

Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents

Zhuoming Chen, Xinrui Zhong, Qilong Feng +5

Sparse attention is becoming increasingly important for serving large language models (LLMs) as generation lengths continue to grow. However, deploying and evaluating new sparse at…

cs.DC2026

Event Tensor: A Unified Abstraction for Compiling Dynamic Megakernel

Hongyi Jin, Bohan Hou, Guanjie Wang +18

Modern GPU workloads, especially large language model (LLM) inference, suffer from kernel launch overheads and coarse synchronization that limit inter-kernel parallelism. Recent me…

cs.PL2026

Prism: Symbolic Superoptimization of Tensor Programs

Mengdi Wu, Xiaoyu Jiang, Oded Padon +1

This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that compactly encodes large class…

cs.CV20251 cited

CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion Models

Xinle Cheng, Zhuoming Chen, Zhihao Jia

Diffusion models have revolutionized generative tasks, especially in the domain of text-to-image synthesis; however, their iterative denoising process demands substantial computati…

cs.CL2025

AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding

Zikun Li, Zhuofu Chen, Remi Delacourt +11

Modern large language model (LLM) applications exhibit diverse service-level objectives (SLOs), from low-latency requirements in interactive coding assistants to more relaxed const…