Jev 决策模型加速 6G 边缘网络意图服务编排
Fast Intent-Driven Service Orchestration with Jev for 6G Edge Networks
做边缘计算或网络调度的可以看这篇：Jev 把意图翻成执行契约再交给数值调度器，决策延迟比 Gemini 低 61.9%。
arXiv 论文把 Jev 用作 6G 边缘网络中意图解释阶段的决策模型，将需求翻译成规定执行位置、期限和优先级的可执行契约，再由数值调度器分配边缘算力。在所有契约解释全部正确的前提下，Jev 的中位决策延迟比 DeepSeek 低 22.4%，比 Gemini 低 61.9%，建模更新场景的完成率分别提升 3.50 和 8.35 个百分点。对比直接使用属性的 Qwen 时，延迟仍降低 53.0%，完成率高出 4.78 个百分点。实验将真实模型响应接入 New Radio 包级仿真、移动性事件、共享边缘队列和真实图像读取服务。在真实图像服务中，各 1,080 个请求里 Jev 按时正确完成 459 个，DeepSeek 为 463 个，Qwen 为 435 个。
Fast Intent-Driven Service Orchestration with Jev for 6G Edge Networks
Intent-driven services envisioned for sixth-generation (6G) edge networks must translate changing requirements into executable contracts while wireless requests continue to arrive. Interpretation consumes part of the same deadline budget as transmission, queueing, and execution. We evaluate Jev as a decision model for this stage, asking whether faster contract activation improves service timeliness while preserving interpretation quality. Contracts specify permitted execution locations, deadlines, and priorities; a numerical scheduler uses them to allocate edge work. The evaluation combines live Jev, DeepSeek, Gemini, and self-hosted Qwen responses with packet-level New Radio simulation, mobility events, shared edge queues, and a real image-reading service. Cached interpretation with numerical scheduling substantially improves completion over direct model-selected placement. With correct interpretation on all evaluated contracts, Jev reduces median decision latency by 22.4% against DeepSeek and 61.9% against Gemini; completion in the modeled update scenarios rises by 3.50 and 8.35 percentage points. A separate comparison with direct-attribute Qwen retains a 53.0% latency reduction and a 4.78-point completion gain. Exchanging recorded model and scheduling waits reproduces the comparator completion summaries for all 12 trajectories in each of two modeled comparisons. In the real image service, Jev retains faster decisions and completes 459 requests correctly and on time, compared with 463 for DeepSeek and 435 for Qwen out of 1,080 each. These findings connect fast intent decisions to timely edge service execution and support using Jev at contract activation, with numerical scheduling adapting placements to current network and computing state.
