English(EN) Drift-Aware LLM Routing with Sparse Contexts and Shared Budgets

新的路由方法在预算约束下优化多模型大语言模型服务

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种名为漂移感知稀疏路由（DRS）的新方法，以在遵守工作负载预算的同时，有效地管理跨多个语言模型的请求。该方法解决了高维提示表示以及由于更新和漂移导致模型性能动态变化的挑战。DRS通过滚动审计窗口估算奖励和资源使用情况，采用悲观奖励和乐观成本估算来做出路由决策。 AI

影响这种路由方法可以提高部署和管理多个大型语言模型的效率和成本效益。

排序理由该集群包含一篇详细介绍大语言模型路由新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍大语言模型路由新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Cheung Hao Lee, Patrick Wong · 2026-09-02 04:00

具有稀疏上下文和共享预算的漂移感知LLM路由

arXiv:2609.00662v1 Announce Type: new Abstract: A multi-model language service must route each request while preserving workload-level budgets for compute, latency, memory, or monetary cost. Two features make this problem materially harder than static model selection. Prompt repr…