AI不再依赖外部设定目标，而是自我生成目标与自我认知。
The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Self
- 提出自驱型AI： agent自主生成目标而非被动执行
- 发现嵌入性是自驱智能的必要条件但不充分
- 揭示自我边界既是行动前提又是认知障碍，适合对意识与智能本质感兴趣的读者
多数人工智能系统假设目标由设计者外生指定。本文探讨当代理开始自我生成目标时所引发的自驱型人工智能（autotelic AI）新范式——代理不仅追求目标，更主动发现目标。通过内在动机、资源驱动先验、因果干预学习、稳态机制及嵌入性等维度展开分析，发现嵌入性虽为自驱代理的必要条件，却非充分条件。嵌入性使代理个体化，却同时揭示个体化非唯一性，同一动态可允许多种有效划分，每种对应不同候选自我。因此，自驱型AI最深层问题不在于如何生成目标，而在于如何生成并相对化归属目标的自我。代理必须相信自身边界才能行动，又需超越边界才能理解。本文整合上述进展形成统一框架，并从三方面拓展：量子形式中代理-环境划分成为物理事实、对非二元冥想传统的哲学批判、以及基于LLM的具象化代理实例。
原文摘要 · Abstract (English)
Most artificial intelligence systems are built on the assumption that goals are exogenous and specified by the designer. Exploring what happens when an agent begins generating its own goals opens the field of autotelic AI. Agents are expected not merely to pursue objectives but to discover them. In this article, we trace its consequences through intrinsic motivation, resource-driven priors, causal-interventional learning, homeostasis, and embeddedness; the last of which is found to be a necessary but not sufficient condition for autotelic agency. Embeddedness individuates the agent at the cost of revealing that the individuation is non-unique, such that the same dynamics admit many valid partitions, each defining a different candidate self. The deepest problem with autotelic AI is therefore not how the agent generates goals, but how it generates and relativizes the self to which the goals are assigned. The agent must believe in its own boundary in order to act, and see through that boundary in order to understand. We consolidate these developments into a single framework and extend it along three directions: a quantum formulation in which the agent-environment cut becomes physical, a philosophical reading against non-dual contemplative traditions, and a concrete LLM-based agentic instantiation.
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