# Ready Cohorts：为LLM智能体控制划定GPU执行机会边界并避免主机往返

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-12 08:00
- AIHOT 分数：47
- AIHOT 链接：https://aihot.virxact.com/items/cmsrcne5g0uburoz2wluecutr
- 原文链接：https://arxiv.org/abs/2608.12123

## AI 摘要

研究为LLM智能体控制路径提出“ready-cohort”边界以量化GPU执行机会。在851会话轨迹回放中，固定分区份额F=30.19%，精确离线份额P*=43.00%，精确打包可恢复固定窗口边界损失的81.83%。将GPU决策保留在设备端，在全部36种配置中均更快，行中位数比率介于1.19x至2.39x。

## 正文

LLM-agent services repeatedly execute small deterministic transitions between model and tool calls: route an outcome, update state, and emit the next effect. We ask when this control path exposes enough concurrent work for GPU execution, and what changes when a GPU-computed route decision remains on device. We formalize the ready-cohort boundary using fixed-partition share F, exact offline share P*, local upper bound U, and online achieved share A. Under zero service time, unlimited capacity, and equal relative launch deadlines, a specialized dynamic program computes P* exactly. In a stationary Poisson replay of one pinned 851-session public trace panel, the primary condition at 100,000 target active sessions, K=256, and a 50 ms launch deadline gives F=30.19%, P*=43.00%, and U=45.85%. Exact packing recovers 81.83% of the opportunity lost at fixed window boundaries. The outcome-derived route key is a conditioning proxy, not proof of executable identity. A separate mechanism study keeps a GPU-computed binary decision on device instead of returning four bytes to the host and redispatching. Across four named GPU placements, the device-resident path is faster in all 36 configurations; within-placement row-median ratios range from 1.19x to 2.39x. Across both admissible mechanisms, all 14,557,440 tested batched invocations match a separately implemented host oracle. A fixed nested device graph that removes no host decision is slower in all 60 configurations across five placements. Together, the studies establish two measurable gates for GPU agent control: deadline-feasible cohort supply and observation placement. A joined finite online runtime is required to measure A, CPU displacement, and service-level benefit.