立场：视觉推理中系统性的能动性缺失
Position: The Systemic Lack of Agency in Visual Reasoning
AI总结：
本文指出当前视觉语言模型因缺乏自主探索能力而无法进行隐式推理，并提出V-IRD基准来评估这一能力，实验表明强语义识别不等同于主动视觉探索。
摘要：
本文论证了系统性的能动性缺失限制了当前视觉语言模型（VLM）的隐式推理能力。隐式推理是指自主发现并利用隐藏的视觉证据来弥合信息鸿沟的能力，而非仅仅依赖明确指定的目标。这种能力是人类视觉理解和日常推理的基础。我们认为，这种限制源于将视觉推理主要视为被动的语义检索，而非依赖于自主视觉探索的主动情境推理。因此，现有大多数基准主要评估被动能力，而忽略了这一推理维度。为弥补这一空白，我们引入了视觉隐式推理诊断基准（V-IRD），该基准通过要求模型严格通过自主视觉分析推导答案来针对这一缺失象限。我们的结果表明，尽管具有强大的检索能力，但主流VLM在利用参考对象和关注需要自主探究的视觉证据方面存在困难。简而言之，强语义识别并不等同于主动视觉探索，揭示了当前VLM的关键差距。更多信息请访问 https://haoychen.github.io/Implicit-Reasoning/
英文摘要：
This paper argues that a systemic lack of Agency constrains the implicit reasoning capabilities of current Vision-Language Models (VLMs). Implicit reasoning refers to the ability to autonomously discover and utilize hidden visual evidence to bridge information gaps, rather than merely relying on explicitly specified targets. This capacity underlies human visual understanding and everyday reasoning. We argue that this limitation arises from a tendency to approach visual reasoning primarily as passive semantic retrieval, rather than as active, situated reasoning that depends on autonomous visual exploration. As a result, most existing benchmarks primarily assess Passive Capacity, leaving this aspect of reasoning largely unmeasured. To address this gap, we introduce the Visual Implicit Reasoning Diagnosing Benchmark (V-IRD), which targets this missing quadrant by requiring models to derive answers strictly through autonomous visual analysis. Our results show that, despite strong retrieval abilities, prominent VLMs struggle to utilize reference objects and to attend to visual evidence that requires self-directed inquiry. Simply put, strong semantic recognition does not equate to active visual exploration, revealing a critical gap in current VLMs. More information can be found at https://haoychen.github.io/Implicit-Reasoning/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
