English(EN) JOVE: Joint Execution and Verification for Resource-Aware LLM Task Graphs

JOVE框架优化LLM任务执行与验证，实现资源效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了JOVE，一个新颖的框架，旨在优化跨不同大型语言模型（LLM）分布的任务的执行和验证。JOVE通过联合分配执行器和选择用于验证的中间输出来解决LLM对特定子任务的适用性未知的问题。该系统在预算和延迟限制下，平衡当前执行成本与为未来改进而学习。通过采用在线学习，根据验证反馈更新LLM质量估计，并纳入信息增益奖励，JOVE旨在改进分配决策。在四个推理基准上的实验表明，JOVE在实现具有竞争力的准确性的同时，与标准推理基线相比，平均成本和延迟降低了至少3.17倍。 AI

影响该框架可能导致更高效、更具成本效益地利用LLM处理复杂的推理任务，从而可能降低运营成本并缩短响应时间。

排序理由该集群包含一篇研究论文，详细介绍了LLM任务执行和验证的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Haoran Zhang, Dongjun Kim, Seohyeon Cha, Kevin S Chan, Ananthram Swami, Gustavo De Veciana, Haris Vikalo · 2026-10-05 04:00

JOVE：面向资源感知的LLM任务图的联合执行与验证

arXiv:2610.03296v1 Announce Type: new Abstract: Complex reasoning queries can be decomposed into directed acyclic task graphs and distributed across heterogeneous LLMs, reducing latency through parallelism and enabling smaller models to solve complex tasks. In practice, however, …

本文如何被排名

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JOVE：面向资源感知的LLM任务图的联合执行与验证

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