SatOps-Decision：用于星载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总结：
该研究提出SatOps-Decision，将Jev式判别式决策模型首次应用于卫星操作，其局部评分器延迟远低于对比模型，准确率优于多数基线，配套首个专用基准。
摘要：
我们提出了SatOps-Decision，一种用于卫星通信操作的低延迟决策方法。据我们所知，这是Jev式通用判别式决策模型首次应用于卫星操作，配套的基准是首个针对该方法的专用基准。所学习的评分器是一个经局部适配的Laya编码器及其原生决策头，它通过单次前向传播从提供的操作答案中进行选择。该基准采用可审计的构建方式，结合了已编写的操作合约、受控状态变化及经独立校验的标签。评估优先考量完整请求延迟和原始决策准确率；所有主要准确率比较均使用1024个保留问题，这些问题独立于训练集和开发集。SatOps-Decision的局部评分器每决策的中位延迟为90.3毫秒，而Qwen3.5-0.8B生成模型为493.9毫秒，OneJev1.6-0.8B为388.4毫秒；在实测的FP32/MPS实现中，基于中位请求时间，分别实现了5.47倍和4.30倍的加速。其平均原始准确率为85.3%，而冻结的Laya为42.5%，Qwen生成模型为40.7%，OneJev为58.6%；作为云端准确率参考，原始Jev 1.14/1.13分别达到87.4%/81.9%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
