把情绪纳入世界模型，让AI预测人类行为更像人。
Large Emotional World Model
- 将情绪设为关键状态变量，分两步预测未来：先情绪后状态。
- 在多个数据集上提升3.94%至45.72%，情绪理解力显著增强。
- 适合研究具身智能、人机交互与情感计算的开发者。
世界由物理规律和情感动态共同支配。仅学习物理规律的世界模型能模拟物理世界，却无法捕捉人类世界。本文引入人类情绪作为世界模型的关键状态变量，使模型既能预测未来状态，也能理解其情感动因。我们构建了首个聚焦情感状态转移的基准数据集EWH，包含10,850条情感感知的转移元组，每条记录前状态、前情绪、动作、后情绪与后状态，支持对行为原因及情绪如何重塑未来的推理。基于EWH，提出大型情感世界模型LEWM，将未来预测分解为两个耦合步骤：先从当前情境预测未来情绪，再以预测情绪为条件进行世界状态预测。实验表明，LEWM在世界状态预测、情绪理解与通用推理任务中均有稳定提升，在EWH上准确率最高提升45.72%，在WorldNet上提升3.94%，在MELD上F1提升17.47%，在特定MMLU类别上提升6.10%。结果证明，将情绪融入世界模型，可实现更真实的人类中心环境模拟，拓展智能体的预测理解能力。
原文摘要 · Abstract (English)
The world is governed by both physical laws and affective dynamics. Physical laws govern state transitions, while affective dynamics shape human actions, decisions, and interactions. A world model that learns only physical laws can approximate the physical world, but not the human world. In this paper, we introduce human emotion as a key state variable in world models, enabling them to capture both future state transitions and their emotional causes. We first construct Emotion-Why-How (EWH), the first world model dataset centered on emotional state transitions, containing 10,850 emotion-aware transition tuples. Each tuple encodes the pre-state, pre-emotion, action, post-emotion, and post-state, supporting reasoning about why actions occur and how emotions reshape future states. Based on EWH, we propose the Large Emotional World Model (LEWM), which factorizes future prediction into two coupled steps: first predicting the future emotional state from the current context, and then conditioning future world-state prediction on the predicted emotion. Experiments show that LEWM brings consistent gains across world-state prediction, emotion understanding, and general reasoning tasks. It achieves up to 45.72% accuracy improvement on EWH, 3.94% on WorldNet, 17.47% F1 improvement on MELD, and a 6.10% gain on specific MMLU categories. These results demonstrate that incorporating emotion into world models enables more realistic simulation of human-centered environments and expands the predictive understanding of intelligent agents.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。