AI 终于开始猜人心了
现在的世界模型只懂物理世界：知道东西在哪、会怎么动，却不知道人心里在想什么。所以它预测人的行为时，常常看着场景对、猜的举动却错。这篇提出「心理世界建模」:把人的信念、欲望、意图、感受、社会规范当成模型的核心变量，和物理状态一起更新，再预测人会怎么做。研究者用 8 个主流大模型在文本、图片、视频故事上测试，发现显式建模心理状态对预测人的决策至关重要。它不是你明天能用上的东西，但这是世界模型从「模拟场景」走向「模拟人心」的第一步。
📄 原文摘要(英文)
World models enable a predictive substrate for planning and action, yet existing formulations merely answer a physical question: what/where it is, and how will it evolve. Human behavior, however, is driven by hidden mental state (what a person believes, wants, intends, feels, and considers socially permissible), so a model that tracks the physical scene but not what each agent knows and believes about it predicts the wrong action for the right-looking scene. We formulate Mental World Modeling (MWM), a generic theoretical framework that makes mental variables core components of a world model rather than posthoc rationales: MWM aintains a coupled physical-mental world state, renders a target-specific partial observation, and simulates how candidate actions jointly update both components. We instantiate the framework in MENTIS, a training-free and fully inspectable baseline that decomposes the process into state parsing, target-observation generation, action decomposition, coupled physical and mental transition, and branch-level value evaluation. On a manually constructed, quality-controlled dataset of situated decision scenarios spanning text, image, and sounding-video stories, experiments with 8 modern LLM-based world models demonstrate that explicitly modeling the mental state is essential for predicting human decisions. Deeper analyses further expose the bottlenecks of current mental world modeling. We expect MWM as a next stage of world modeling, from simulating physical scenes to simulating the minds that act in them.