---
title: "SatOps-Decision: Low-Latency Discriminative Decisions for Onboard Satellite Communication Operations"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.38697v1"
permalink: "https://feed.astralor.com/items/itm_2831acb672dc663f9adcf384"
published: "2026-10-28T19:00:56.000Z"
score: 54
channel: "tech"
tags: ["推理效率","SatOps-Decision","Low-Latency Discriminative Decisions","Onboard Satellite Communication","Operations","Jev-style"]
---

# SatOps-Decision: Low-Latency Discriminative Decisions for Onboard Satellite Communication Operations

## 速览

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…

## 关键点

- 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.

## 全文

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%.

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