Unmanned Aerial Vehicles (UAVs) in disaster response require complex, queryable intelligence that on-board CNNs cannot provide. While Vision-Language Models (VLMs) offer this semantic reasoning, their high resource demands make on-device deployment infeasible, and naive cloud offloading fails under the low-bandwidth networks common in disaster zones. We present AVERY, a framework that enables VLM deployment through adaptive split computing. We advance the split computing paradigm beyond traditional depth-wise partitioning by introducing a functional, cognitive-inspired dual-stream split that separates the VLM into a high-frequency, low-resolution "context stream" for real-time awareness and a low-frequency, high-fidelity "insight stream" for deep analysis. A lightweight, self-aware on-board controller manages this architecture, monitoring network conditions and operator intent to dynamically select from pre-trained compression models, navigating the fundamental accuracy-throughput trade-off. Evaluated using the VLM LISA-7B across an edge-cloud scenario under fluctuating network conditions, AVERY consistently outperforms static configurations, achieving 11.2% higher accuracy than raw image compression and 93.98% lower energy consumption compared to full-edge execution, thereby enhancing mission efficiency and enabling real-time, queryable intelligence on resource-constrained platforms in dynamic environments.

翻译：在灾害响应中，无人机需要复杂且可查询的智能分析能力，而机载卷积神经网络无法满足这一需求。尽管视觉-语言模型能够提供此类语义推理，但其高资源需求使得在设备端部署变得不可行，而简单的云端卸载在灾害区域常见的低带宽网络环境下也会失效。本文提出AVERY框架，通过自适应分割计算实现VLM的部署。我们推进了分割计算范式，超越传统的深度分割方法，引入了一种功能化、认知启发的双流分割策略：将VLM分离为高频、低分辨率的“上下文流”用于实时感知，以及低频、高保真的“洞察流”用于深度分析。一个轻量级的、具备自我感知能力的机载控制器管理此架构，监测网络条件和操作者意图，动态选择预训练的压缩模型，以在准确性与吞吐量之间进行权衡优化。通过在波动网络条件下的边缘-云端场景中使用VLM LISA-7B进行评估，AVERY始终优于静态配置，相比原始图像压缩方法准确率提升11.2%，与全边缘执行相比能耗降低93.98%，从而提升了任务效率，并在动态环境中为资源受限平台实现了实时、可查询的智能分析能力。