MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning

AI 中文解读

先说说这次最抓人的点：研究人员给AI出了一套“看图猜心思”的考题，结果发现所有顶尖AI全都“翻车”了——它们能认出图片里的东西，却看不懂角色为什么这么做。具体来说，这套名叫MultivationBench的新测试，不考AI看静态图片，而是让它看一段有剧情发展的连环画，并结合前后情节推断人物动机。就像你看到一个人先饿着肚子，后来抢了面包，能猜到他是为了填饱肚子。但AI却做不到这种层层递进的推理，它只能分析单张画面，无法把前因后果串联起来。测试基于马斯洛需求层次和瑞斯基本欲望等心理学理论设计，覆盖了人类常见的各种行为驱动力。这项研究给普通人带来的启示是：目前AI虽然能“看见”世界，但还远不能“理解”人心。将来如果AI想真正融入日常生活，比如做你的生活助理、情感陪伴者，或者帮你分析一桩复杂的人际关系，它就得学会这种根据线索推测意图的能力。否则，AI永远只能给出冷冰冰的表面答案，无法成为真正懂你的伙伴。这次研究相当于给行业敲了一记警钟，也指明了未来努力的方向。

arXiv:2607.26465v1 Announce Type: new Abstract: Multimodal Large Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform sequential motivation reasoning remains insufficiently studied. Existing evaluations predominantly examine static text or isolated visual snapshots, which do not reflect the cumulative nature of real-world behavioral drivers. To address this gap, we introduce MultivationBench, a benchmark designed to rigorously evaluate multimodal motivation reasoning within story-driven visual narratives. The benchmark builds upon established psychological frameworks - Maslow's hierarchy and Reiss's basic desires - and requires models to integrate accumulated multimodal context to infer evolving motivations. Results indicate that MultivationBench presents a significant challenge: all tested models struggle to maintain consistent motivation reasoning across sequential contexts, revealing a critical disconnect between static recognition capabilities and the dynamic reasoning essential for human-like social understanding.