构建杀伤链：一种零样本框架，用于边缘节点上的目标验证和战术推理
Synthesizing the Kill Chain: A Zero-Shot Framework for Target Verification and Tactical Reasoning on the Edge
AI总结：
本文提出了一种零样本框架，通过边缘设备实现目标验证和战术推理，验证了分层架构在动态军事环境中的有效性。
摘要：
在动态军事环境中部署自主边缘机器人受到领域特定训练数据稀缺和边缘硬件计算限制的制约。本文介绍了一种分层的零样本框架，该框架将轻量级目标检测与来自Qwen和Gemma家族的紧凑型视觉-语言模型（VLMs）（4B-12B参数）进行级联。Grounding DINO用作高召回率、可文本提示的区域提出者，高检测置信度的框被传递给边缘类VLMs进行语义验证。我们在Battlefield 6的55个高保真合成视频上评估了该流程，三个任务上分别达到最高100%的假阳性过滤精度、最高97.5%的损害评估精度和55-90%的细粒度车辆分类精度。我们进一步将该流程扩展为一个代理Scout-Commander工作流程，实现了100%正确的资产部署，并在子75秒延迟内达到9.8/10的推理分数（由GPT-4o评分）。一种新的“受控输入”方法将感知与推理解耦，揭示了不同的失败表型：Gemma3-12B在战术逻辑上表现优异但视觉感知失败，而Gemma3-4B在输入准确的情况下推理崩溃。这些发现验证了边缘自主的分层零样本架构，并提供了一种用于安全关键应用中VLM适用性的诊断框架。
英文摘要：
Deploying autonomous edge robotics in dynamic military environments is constrained by both scarce domain-specific training data and the computational limits of edge hardware. This paper introduces a hierarchical, zero-shot framework that cascades lightweight object detection with compact Vision-Language Models (VLMs) from the Qwen and Gemma families (4B-12B parameters). Grounding DINO serves as a high-recall, text-promptable region proposer, and frames with high detection confidence are passed to edge-class VLMs for semantic verification. We evaluate this pipeline on 55 high-fidelity synthetic videos from Battlefield 6 across three tasks: false-positive filtering (up to 100% accuracy), damage assessment (up to 97.5%), and fine-grained vehicle classification (55-90%). We further extend the pipeline into an agentic Scout-Commander workflow, achieving 100% correct asset deployment and a 9.8/10 reasoning score (graded by GPT-4o) with sub-75-second latency. A novel "Controlled Input" methodology decouples perception from reasoning, revealing distinct failure phenotypes: Gemma3-12B excels at tactical logic but fails in visual perception, while Gemma3-4B exhibits reasoning collapse even with accurate inputs. These findings validate hierarchical zero-shot architectures for edge autonomy and provide a diagnostic framework for certifying VLM suitability in safety-critical applications.
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。
