Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents
Sparse attention is becoming increasingly important for serving large language models (LLMs) as generation lengths continue to grow. However, deploying and evaluating new sparse attention algorithms at scale remains highly engineering-intensive, slowing both human researchers and AI agents in exploring the sparse attention design. To address this challenge, we present Vortex, a system that combines a Python-embedded frontend language atop a page-centric tensor abstraction for expressing a broad range of sparse attention algorithms, with an efficient backend tightly integrated into modern LLM serving stacks. Vortex enables rapid prototyping, deployment, and evaluation of sparse attention a...
| Authors | Zhuoming Chen, Xinrui Zhong, Qilong Feng et al. |
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| Primary category | cs.AI |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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