ENC-Bench：评估多模态大语言模型电子海图理解能力的基准
ENC-Bench: A Benchmark for Evaluating Multimodal Large Language Models in Electronic Navigational Chart Understanding
该基准揭示了MLLMs在专业符号系统（如海图矢量符号）和几何空间计算上的系统性缺陷，为视觉-语言模型在安全关键领域的符号接地与多约束推理提供了可复用的评测框架和失败模式分析。
原文依据 The best model achieves only 47.88% accuracy, with systematic challenges in symbolic grounding, spatial computation, multi-constraint reasoning
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论电子海图（ENC）是现代海上导航中安全关键的骨干，然而多模态大语言模型（MLLM）能否可靠地解读它们仍不清楚。与自然图像或传统图表不同，电子海图通过标准化矢量符号、尺度依赖渲染和精确几何结构来编码法规、水深和航线约束——需要专业海事知识才能解读。我们提出了ENC-Bench，首个专门用于专业电子海图理解的基准。ENC-Bench包含来自840份真实美国国家海洋和大气管理局（NOAA）电子海图的20,490个专家验证样本，组织为三层级结构：感知（符号与特征识别）、空间推理（坐标定位、方位、距离）和海事决策（多约束下的航线合法性、安全评估、应急规划）。所有样本均通过校准的矢量到图像流水线从原始S-57数据生成，并经过自动一致性检查和专家审核。我们在统一的零样本协议下评估了10个最优MLLM，如GPT-4o、Gemini 2.5、Qwen3-VL、InternVL-3和GLM-4.5V。最佳模型仅达到47.88%的准确率，在符号接地、空间计算、多约束推理以及对光照和尺度变化的鲁棒性方面存在系统性挑战。通过建立首个严格的电子海图基准，我们开辟了专业符号推理与安全关键AI交叉的新研究前沿，为推进MLLM走向专业海事应用提供了必要的基础设施。
英文原文摘要 1678 字符
Electronic Navigational Charts (ENCs) are the safety-critical backbone of modern maritime navigation, yet it remains unclear whether multimodal large language models (MLLMs) can reliably interpret them. Unlike natural images or conventional charts, ENCs encode regulations, bathymetry, and route constraints via standardized vector symbols, scale-dependent rendering, and precise geometric structure---requiring specialized maritime expertise for interpretation. We introduce ENC-Bench, the first benchmark dedicated to professional ENC understanding. ENC-Bench contains 20,490 expert-validated samples from 840 authentic National Oceanic and Atmospheric Administration (NOAA) ENCs, organized into a three-level hierarchy: Perception (symbol and feature recognition), Spatial Reasoning (coordinate localization, bearing, distance), and Maritime Decision-Making (route legality, safety assessment, emergency planning under multiple constraints). All samples are generated from raw S-57 data through a calibrated vector-to-image pipeline with automated consistency checks and expert review. We evaluate 10 state-of-the-art MLLMs such as GPT-4o, Gemini 2.5, Qwen3-VL, InternVL-3, and GLM-4.5V, under a unified zero-shot protocol. The best model achieves only 47.88% accuracy, with systematic challenges in symbolic grounding, spatial computation, multi-constraint reasoning, and robustness to lighting and scale variations. By establishing the first rigorous ENC benchmark, we open a new research frontier at the intersection of specialized symbolic reasoning and safety-critical AI, providing essential infrastructure for advancing MLLMs toward professional maritime applications.