Intent-Based Networking (IBN) allows operators to specify high-level network goals rather than low-level configurations. While recent work demonstrates that large language models can automate configuration tasks, a distinct class of intents requires generating optimization code to compute provably optimal solutions for traffic engineering, routing, and resource allocation. Current systems assume text-based intent expression, requiring operators to enumerate topologies and parameters in prose. Network practitioners naturally reason about structure through diagrams, yet whether Vision-Language Models (VLMs) can process annotated network sketches into correct optimization code remains unexplored. We present IntentOpt, a benchmark of 85 optimization problems across 17 categories, evaluating four VLMs (GPT-5-Mini, Claude-Haiku-4.5, Gemini-2.5-Flash, Llama-3.2-11B-Vision) under three prompting strategies on multimodal versus text-only inputs. Our evaluation shows that visual parameter extraction reduces execution success by 12-21 percentage points (pp), with GPT-5-Mini dropping from 93% to 72%. Program-of-thought prompting decreases performance by up to 13 pp, and open-source models lag behind closed-source ones, with Llama-3.2-11B-Vision reaching 18% compared to 75% for GPT-5-Mini. These results establish baseline capabilities and limitations of current VLMs for optimization code generation within an IBN system. We also demonstrate practical feasibility through a case study that deploys VLM-generated code to network testbed infrastructure using Model Context Protocol.

翻译：意图驱动网络允许操作员指定高层次网络目标，而非低层配置。尽管近期研究表明大型语言模型可自动化配置任务，但存在一类特殊意图需要生成优化代码，以便为流量工程、路由和资源分配问题计算可证明的最优解。现有系统通常假设意图以文本形式表达，要求操作员用文字描述拓扑和参数。网络从业者惯于通过图表理解结构，然而视觉语言模型能否将带标注的网络示意图转化为正确的优化代码仍是未知领域。本文提出IntentOpt基准测试集，涵盖17个类别的85个优化问题，在三种提示策略下评估四种VLM模型（GPT-5-Mini、Claude-Haiku-4.5、Gemini-2.5-Flash、Llama-3.2-11B-Vision）处理多模态与纯文本输入的表现。评估表明：视觉参数提取使执行成功率降低12-21个百分点，其中GPT-5-Mini从93%降至72%；思维链编程提示导致性能下降达13个百分点；开源模型表现滞后于闭源模型，Llama-3.2-11B-Vision仅达18%，而GPT-5-Mini为75%。这些结果为当前VLM在IBN系统中生成优化代码的能力与局限建立了基准。我们通过案例研究进一步验证了实际可行性：利用模型上下文协议将VLM生成的代码部署至网络测试平台基础设施。