arXiv cs.AI· Lifan Mei, Yuxuan Li, Jiaxian Tu, Shenghe Xu·· 14 小时前AI 评分23
SatOps-Decision：面向星上卫星通信操作的低延迟判别式决策方法
SatOps-Decision: Low-Latency Discriminative Decisions for Onboard Satellite Communication Operations
AI 导读
SatOps-Decision 将 Jev 风格通用判别式决策模型首次用于卫星通信操作，其本地适配的 Laya 编码器配合原生决策头，单次前向即可从候选操作答案中选出结果。
正文 · 原文
Abstract:We propose SatOps-Decision, a method for low-latency decisions in satellite communication operations. To the best of our knowledge, it is the first application of Jev-style general-purpose discriminative decision models to satellite operations, and the accompanying benchmark is the first dedicated to this approach. The learned scorer, a locally adapted Laya encoder with its native decision head, selects among supplied operational answers in a single forward pass. The benchmark combines authored operational contracts, controlled state changes and independently checked labels in an auditable construction. Evaluation prioritizes complete-request latency and raw decision accuracy; all main accuracy comparisons use the same 1,024 held-out questions, separate from training and development. SatOps-Decision's local scorer has a median latency of 90.3 ms per decision, versus 493.9 ms for Qwen3.5-0.8B generation and 388.4 ms for OneJev1.6-0.8B: $5.47\times$ and $4.30\times$ speedups based on median request times in the measured FP32/MPS implementations. Its mean raw accuracy is 85.3%, versus 42.5% for frozen Laya, 40.7% for Qwen generation and 58.6% for OneJev. As cloud accuracy references, original Jev 1.14/1.13 reach 87.4/81.9%.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2610.38697 [cs.LG] |
| (or arXiv:2610.38697v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38697 arXiv-issued DOI via DataCite |
Submission history
From: Lifan Mei [view email]
[v1]
Wed, 28 Oct 2026 19:00:56 UTC (591 KB)
来源：arXiv cs.AI · arxiv.org