SatOps-Decision：90.3 毫秒低延迟星载卫星通信决策模型
SatOps-Decision: Low-Latency Discriminative Decisions for Onboard Satellite Communication Operations
一篇把判别式决策模型搬到卫星通信上的论文，单次前向 90 毫秒出决策，准确率还比生成式基线高出一大截，做边缘部署的可以看看。
论文提出 SatOps-Decision，将 Jev 风格的判别式决策模型用于卫星通信在轨运营决策。其本地打分器基于 Laya 编码器加原生决策头，单次前向传播即可在候选运营答案中完成选择。在 1,024 道独立留出题上，其中位延迟为 90.3 毫秒，比 Qwen3.5-0.8B 生成的 493.9 毫秒快 5.47 倍，比 OneJev1.6-0.8B 快 4.30 倍；平均原始准确率达 85.3%，高于 Qwen 生成的 40.7% 和 OneJev 的 58.6%。配套基准包含人工撰写的运营合同、受控状态变化与独立核查标签，云上 Jev 1.14/1.13 分别取得 87.4%/81.9% 作为参考。
SatOps-Decision: Low-Latency Discriminative Decisions for Onboard Satellite Communication Operations
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%.