大规模情感世界模型 (Large Emotional World Model)

World Models serve as tools for understanding the current state of the world and predicting its future dynamics, with broad application potential across numerous fields. As a key component of world knowledge, emotion significantly influences human decision-making. While existing Large Language Models (LLMs) have shown preliminary capability in capturing world knowledge, they primarily focus on modeling physical-world regularities and lack systematic exploration of emotional factors. In this paper, we first demonstrate the importance of emotion in understanding the world by showing that removing emotionally relevant information degrades reasoning performance. Inspired by theory of mind, we further propose a Large Emotional World Model (LEWM). Specifically, we construct the Emotion-Why-How (EWH) dataset, which integrates emotion into causal relationships and enables reasoning about why actions occur and how emotions drive future world states. Based on this dataset, LEWM explicitly models emotional states alongside visual observations and actions, allowing the world model to predict both future states and emotional transitions. Experimental results show that LEWM more accurately predicts emotion-driven social behaviors while maintaining comparable performance to general world models on basic tasks.

翻译：世界模型作为理解当前世界状态并预测其未来动态的工具，在众多领域具有广泛的应用潜力。作为世界知识的关键组成部分，情感对人类决策具有显著影响。尽管现有的大语言模型（LLMs）已展现出捕捉世界知识的初步能力，但其主要聚焦于对物理世界规律建模，缺乏对情感因素的系统性探索。本文首先通过实验证明：移除情感相关信息会降低推理性能，从而阐明情感在理解世界中的重要性。受心理理论启发，我们进一步提出了大规模情感世界模型（LEWM）。具体而言，我们构建了情感-原因-方法（EWH）数据集，该数据集将情感融入因果关系中，支持对行为发生原因及情感如何驱动未来世界状态的推理。基于此数据集，LEWM在视觉观测与行为的基础上显式建模情感状态，使世界模型能够同时预测未来状态与情感演变。实验结果表明，LEWM在基础任务上保持与通用世界模型相当性能的同时，能更准确地预测情感驱动的社会行为。