多模态AI学不会看图学知识：新基准揭示能力断层
AI模型在考试中表现不错，但遇到需要从图片、表格、地图等真实场景中现场学习新知识的任务时，几乎全部翻车。研究者构建了CLBench-V基准，包含3443个跨科学、金融、空间推理等领域的多模态任务，测试模型能否像人一样从图文混合的上下文里提取信息并应用。结果最强模型（InternVL3.5）得分仅0.2847（满分1），连简单的地图导航或财报图表理解都频繁出错。这不是你明天能用上的工具，但它划了一条线：当前AI的“聪明”高度依赖训练数据里的知识，一旦需要现场看图学新规则，能力就断崖下跌。
📄 原文摘要(英文)
Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge. While recent work has highlighted this capability as context learning, existing evaluations mainly focus on textual contexts. In many practical settings, however, the context to be learned from is multimodal: scientific findings are conveyed through figures and tables, financial indicators are scattered across converted reports, and spatial decisions depend on maps, scenes, or web pages. We introduce CLBench-V, a benchmark for multimodal context learning that addresses the difficulty of localizing where context use breaks down by organizing tasks around three dimensions: context grounding, new information application, and new knowledge learning. CLBench-V combines converted public benchmarks with newly constructed datasets spanning domains such as science, finance, long-document understanding, spatial reasoning, and web-based visual question answering. To reduce the cost of constructing domain-specific context-learning tasks, we further use automated construction and filtering procedures for our newly built datasets. Across 3,443 instances and six recent multimodal models, the best overall score is only 0.2847, indicating that multimodal context learning remains far from saturated. Moreover, InternVL3.5-30B-A3B performs best on context grounding and new knowledge learning, while Qwen3.5-Plus performs best on new information application. We further analyze judge reliability, context length, image count, and representative failure cases. Code is available at https://github.com/IamLihua/CLBench-V.