# AI智能体模型批判--从笛卡尔思想到GIC通用架构

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-22 08:00
- AIHOT 分数：36
- AIHOT 链接：https://aihot.virxact.com/items/cmqsov9ha04zlslfuhtuykrjv
- 原文链接：https://arxiv.org/abs/2606.23991

## AI 摘要

论文基于笛卡尔独立思想奠基与科幻自主存在体描绘，从目标、身份、决策、自我调节与学习五维度剖析当前AI智能体架构。区分能力来自外部工程组装的agentic系统与能力（含社交互动）内生的agentive系统。提出Goal-Identity-Configurator（GIC）通用架构，融合分层目标分解、身份演化、基于世界模型的模拟推理、习得性自我调节与自我导向学习，并讨论agentive系统在人类监督下的可审计性、可控性与安全性。

## 正文

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 human control with destructive power under a speculative ``machine agency" against humans, it has become essential to clarify where automation ends and agency begins, both for building capable systems and for understanding whether and what to fear. Drawing on Descartes' grounding of agency in independent thought, and on portrayals of autonomous beings in science fiction, we survey the current landscape of AI agents, and analyze agent architectures along five dimensions: goal, identity, decision-making, self-regulation, and learning. Specifically, we argue that genuine agency requires these structures to be internalized within the system itself rather than assembled through external scaffolding. This distinction between agentic systems, whose competence resides in engineered workflows, and agentive systems, whose capabilities (including social interaction) arise endogenously, defines the boundary between systems designed for prescribed tasks, and those capable of operating in the open world with true autonomy. Building on this analysis, we propose the Goal-Identity-Configurator (GIC) architecture for a general-purpose agent model, combining hierarchical goal decomposition, identity evolution, simulative reasoning grounded in a separately trained world model, learned self-regulation, and self-directed learning from both real and simulated experience. Furthermore, we share insight on the auditability, controllability, and safety of agentive systems that possess greater autonomy and ``agency", but remain under human oversight.