新研究测试视觉语言模型时长常识推理，多轮思维链也难奏效
Leaving, Staying, or Living There? Duration Commonsense Reasoning with Human-LLM Co-annotations
论文让模型判断'后院种花是常住还是 Airbnb 是短住'这类时长问题，主流模型集体翻车，分析写得挺细。
一篇论文构建了人机协同标注的地理标记推文语料库，测试视觉语言模型能否推断事件时长。结果显示主流模型表现不佳，即使使用多轮 Chain-of-Thought 提示也难以改善。错误分析发现模型常忽略上下文信息，或过度依赖显眼视觉细节而错过标签等语义线索。该语料库可作为时长常识推理的测试基准。
Leaving, Staying, or Living There? Duration Commonsense Reasoning with Human-LLM Co-annotations
Gardening in the backyard implies living there, while checking into an Airbnb more likely means staying for a few days. Such duration judgments are important for understanding human mobility, travel behavior, and social context. However, duration is rarely stated explicitly in social media posts and must instead be inferred from noisy textual, visual, and contextual cues. In this paper, we investigate whether vision-language models possess such duration commonsense. We create a new corpus of geotagged tweets co-annotated by humans and LLMs to serve as a testbed for this task. We find that state-of-the-art models struggle with duration inference, even when guided by multi-turn Chain-of-Thought prompting. Error analyses further reveal that models often fail to integrate critical contextual information, or they overly rely on prominent visual details while overlooking subtle hashtags and semantic nuances. These findings underscore the significant challenges remaining in context-aware multimodal understanding.
