让智能体主动构建和优化自身知识，提升适应能力
Agent-centric learning: from external reward maximization to internal knowledge curation
- 以内部表征为核心，衡量智能体自我掌控知识的能力
- 通过内化知识结构增强应对未知环境的准备度
- 适合研究通用智能与自适应系统的设计者
通用智能的研究传统聚焦于外部目标：智能体对环境的控制力或特定任务的完成度。然而，这种外部导向易导致智能体高度专业化而缺乏适应性。本文提出表征赋能（representational empowerment），一种全新的以智能体为中心的学习范式，将控制权转向内部。该目标衡量智能体有意识地维持和多样化自身知识结构的能力。我们主张，塑造自身理解的能力是实现更高‘准备度’的关键，独立于直接环境影响。将内部表征作为计算赋能的主要载体，为设计更具适应性的智能系统提供了新视角。
原文摘要 · Abstract (English)
The pursuit of general intelligence has traditionally centered on external objectives: an agent's control over its environments or mastery of specific tasks. This external focus, however, can produce specialized agents that lack adaptability. We propose representational empowerment, a new perspective towards a truly agent-centric learning paradigm by moving the locus of control inward. This objective measures an agent's ability to controllably maintain and diversify its own knowledge structures. We posit that the capacity -- to shape one's own understanding -- is an element for achieving better ``preparedness'' distinct from direct environmental influence. Focusing on internal representations as the main substrate for computing empowerment offers a new lens through which to design adaptable intelligent systems.
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。