一篇新的学术论文提出了一个理解和构建 AI 代理的框架，区分了依赖外部脚手架的“agentic”系统和具有内在能力的“agentive”系统。该论文介绍了目标-身份-配置器（GIC）架构，旨在实现 AI 代理的真正自主性和内生能力。作者们还讨论了日益自主的 AI 系统的可审计性、可控性和安全性方面的影响。 AI
影响 阐明了工程化 AI 工作流与真正 AI 自主性之间的区别，影响了未来的代理开发和安全研究。
排序理由 该集群包含一篇讨论 AI 代理架构并提出新模型的学术论文。
在 arXiv cs.MA (Multiagent) 阅读 →
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2606.23991v1 Announce Type: new Abstract: What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'', and other ``agentic" tools that promise to drive up productivity, and at the same ti…
What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'', and other ``agentic" tools that promise to drive up productivity, and at the same time, ``existential" concerns such as AI escaping …
True artificial agency requires internalized structures for goals, identity, decision-making, self-regulation, and learning, distinguishing autonomous systems from task-specific ones.