多模态AI学新东西有多难？最新评测：最高分不到30%
AI能像人一样从图片、表格、网页里现场学新知识吗？最新基准测试CLBench-V发现，最强模型综合得分仅0.2847（满分1）。测试覆盖科学图表、金融报告、地图场景等真实场景，要求模型先定位上下文（比如图中数据点），再应用新信息（比如根据图表算趋势），最后学会新知识（比如理解一个新概念）。结果：模型在“定位”和“学新”上各有擅长，但整体远未及格。这不是你明天能用上的工具，但它划清了当前多模态AI的边界：它们能认出猫狗，但面对一张陌生图表里的新规则，基本抓瞎。
📄 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.