发表机构
University of California, Berkeley; University of Tübingen; TU Darmstadt（加州大学伯克利分校; 蒂宾根大学; 达姆施塔特工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出表征赋能（RepEmp）框架，用于智能体持续模型构建，通过分层管理者-行动者架构在三类实验中验证其能提升模型结构恢复、跨任务迁移及泛化能力。
AI 中文摘要
建模世界的首要问题并非仅估计正确的参数或因果结构，而是决定究竟应当表征什么。我们将该问题框架为持续模型构建：智能体维护一个针对不可达世界W的特定环境模型M，并在不同环境间整理一个可复用表征元素的持久库L。我们提出表征赋能（Representational Empowerment, RepEmp），通过候选元素拓展智能体未来建模与规划能力的程度对其评分，补充经典的赋能定义，但将其重新定义为对内部表征而非外部状态的控制。我们将该框架实现为分层的管理者-行动者（Curator-Actor）架构，并在三个实验中进行测试。在闭词汇因果学习任务中，人类参与者构建不同抽象粒度的因果模型以最大化目标可达性，而非对世界的保真度，RepEmp相比信息增益替代方法能更好地预测这一特征。匹配的模拟显示，RepEmp引导的构建相比探索对充分结构恢复和跨任务迁移的贡献更大。最后，在开词汇规划领域，大语言模型（LLM）增强的管理者构建出更紧凑的符号库，其泛化能力也优于基线方法。移除RepEmp会消除这些益处。综上，这些结果确定RepEmp是持续模型构建的关键原则：在有限资源下决定构建、保留和复用的内容。
英文摘要
The first problem of modeling the world is not just estimating the right parameters or causal structure, but deciding what should be represented at all. We frame this problem as continual model construction: an agent maintains an environment-specific model M of an inaccessible world W and curates a persistent library L of reusable representational elements across environments. We propose Representational Empowerment (RepEmp) to score candidate elements by how much they expand the agent's future capacity to model and plan, complementing the classic definition of empowerment, but redefined as control over internal representations instead of external states. We realize the framework as a hierarchical Curator-Actor architecture and test it across three experiments. In a closed-vocabulary causal-learning task, human participants construct causal models at varying abstraction granularities to maximize goal reachability rather than fidelity to the world, a signature better predicted by RepEmp than by information-gain alternatives. Matched simulations reveal that RepEmp-guided construction contributes more than exploration to sufficient structure recovery and cross-task transfer. Finally, in an open-vocabulary planning domain, an LLM-augmented Curator builds more compact symbolic libraries, which also generalize better than baselines. Ablating RepEmp eliminates these benefits. Together, these results identify RepEmp as a key principle for continual model construction: deciding what to build, retain, and reuse under bounded resources.
