Unmanned Aerial Vehicles (UAVs) in disaster response require complex, queryable intelligence that onboard 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, unstable networks endemic to disaster zones. We present AVERY, an intent-driven adaptive split computing framework for efficient VLM deployment on resource-constrained platforms. AVERY is motivated by the observation that operator intent must be treated as a first-class system objective, since missions such as broad situational monitoring and precise, spatially grounded investigation require different semantic products, latency targets, and resource allocations. To reflect this, AVERY advances split computing beyond traditional depth-wise partitioning through a functional, cognitive-inspired dual-stream split: a high-frequency, low-resolution Context stream for real-time awareness, and a low-frequency, high-fidelity Insight stream for deep analysis. This design enables a hierarchical split strategy: computation is first separated by function, then partitioned depth-wise across edge and cloud when the Insight stream is required. A lightweight, self-aware onboard controller monitors network conditions and operator intent to select from pre-trained compression models, navigating the accuracy-throughput trade-off at runtime. Evaluated using LISA-7B in an edge-cloud setting under fluctuating network conditions, AVERY achieves 11.2% higher accuracy than raw image compression, 93.98% lower energy consumption than full-edge execution, and average accuracy within 0.75% of the static High-Accuracy baseline during dynamic adaptation. Overall, AVERY enhances mission efficiency and enables real-time, queryable intelligence in dynamic disaster environments.

翻译：摘要：灾害响应中的无人机（UAV）需要复杂的可查询智能，而机载CNN无法提供此类能力。尽管视觉语言模型（VLM）具备语义推理能力，但其高资源需求使设备端部署不可行，而灾害区域普遍存在的低带宽、不稳定网络条件下，简单的云端卸载方案也会失效。本文提出AVERY——一种面向资源受限平台的意图驱动自适应拆分计算框架，用于高效部署VLM。AVERY的提出源于以下观察：操作员意图必须作为首要系统目标对待，因为宽泛态势监控与精确空间定位调查等任务需要不同的语义产物、延迟目标和资源分配。为此，AVERY通过功能化、认知启发的双流拆分机制，将传统深度维度的拆分计算改进为：高频低分辨率上下文流（Context）实现实时感知，低频高保真洞察流（Insight）进行深度分析。该设计支持分层拆分策略：计算首先按功能分离，当需要Insight流时再沿深度维度在边缘与云端间进行分区。轻量级自感知机载控制器实时监测网络条件与操作员意图，从预训练压缩模型中选择适配方案，在运行时权衡准确率与吞吐量。在波动网络条件下使用LISA-7B在边缘-云环境中评估，AVERY相比原始图像压缩准确率提升11.2%，相比全边缘执行能耗降低93.98%，动态适应期间平均准确率与静态高精度基线相差仅0.75%。整体而言，AVERY可提升任务效率，在动态灾害环境中实现实时可查询智能。