arXiv:2606. 01502v1 Announce Type: cross Abstract: Frontier LLMs increasingly decide what a query attends to with a sparse-attention indexer that picks a few KV-cache blocks per query: attention's unit is now a small, reusable chunk.
By Bole Ma, Jan Eitzinger, Harald K\"ostler, Gerhard Wellein
arXiv:2608. 13057v1 Announce Type: cross Abstract: In expert-parallel (EP) MoE serving, every layer synchronizes at the slowest GPU.
By Jie Li, Chenxin Jia, Jinliang Shen, Cunzhuang Liu, Ruiyi Ding, Jianwen Xian, Kang He, Chengru Song
arXiv:2608. 11231v1 Announce Type: new Abstract: LLM serving is increasingly accelerated by position-independent caching (PIC).
By Yirui Liu, Ruoling Qi, Longwen Wang, Xuaner Wu, Jian Chen, Yuxin Jin, Jiawei Shao, Xuelong Li
arXiv:2606. 13361v1 Announce Type: new Abstract: Right now, across the world, AI agents are repeating the same absurd act: to read one document, they each recompute it from scratch.
By Luoyuan Zhang
arXiv:2606. 20474v1 Announce Type: cross Abstract: Context-heavy agents place unusual pressure on the key-value (KV) cache: long prefixes are reused across many short turns, while concurrency determines whether the serving system can keep GPUs utilized.
By Inesh Chakrabarti (Advanced Micro Devices, University of California, Los Angeles), David Limpus (Advanced Micro Devices, Purdue University), Aditi Ghai Rana (Advanced Micro Devices), Bowen Bao (Advanced Micro Devices), Spandan Tiwari (Advanced Micro Devices), Thiago Crepaldi (Advanced Micro Devices), Ashish Sirasao (Advanced Micro Devices)
arXiv:2607. 05876v1 Announce Type: cross Abstract: LLM serving optimization typically benchmarks many configurations and reaches for heavy profilers when latency targets are missed.
By Yihua Liu