论文

AsymCache 是一个面向 LLM 推理的计算-延迟感知 KV cache 管理系统，将 cache 驻留决策与 GPU attention kernel 性能显式对齐，包含三个关键组件：用于高效处理非连续 KV 上下文的多段注意力（MSA）、联合优化命中率与位置感知重计算代价的 cache 淘汰策略，以及面向高硬件利用率的自适应分片调度器。AsymCache is proposed, a computation-latency-aware KV cache management system for LLM inference that explicitly aligns cache residency decisions with GPU attention kernel performance, including three key components: Multi-Segment Attention (MSA) for efficient non-contiguous KV context processing, a cache eviction policy that jointly optimizes hit rate and position-aware recomputation cost, and an adaptive chunking scheduler for high hardware utilization.

这篇立场论文认为，LLM 推理 serving 已超越通用启发式方法，如今需要数学优化与算法基础，并呼吁社区将 LLM serving 的算法设计视为一个新的研究前沿。This position paper argues that LLM inference serving has outgrown generic heuristics and now demands mathematical optimization and algorithmic foundations, and calls on the community to recognize algorithmic design for LLM serving as a research frontier.

本文通过收益–成本视角研究 PLT 循环次数选择：额外循环可精炼表示，但 CLP 也会在每次循环边界引入位置错配，由此解释 PLT 在两次循环时趋于饱和的现象，并为循环次数选择提供诊断依据。This study studies PLT loop-count selection through a gain--cost view: an extra loop may refine representations, but CLP also introduces a positional mismatch at each loop boundary, explaining PLT's saturation at two loops and providing diagnostics for loop-count selection.