Agentic AI比单一模型扩展更可能通向通用人工智能
Position: Agentic AI System Is a Foreseeable Pathway to AGI
- 用有向无环图结构构建智能体系统，替代单一大模型
- 实验显示智能体系统泛化能力与样本效率呈指数级提升
- 适合研究通用人工智能和多任务协同的学者关注
单一模型的持续扩展是否是实现通用人工智能的唯一路径？本文挑战这一主流观点，指出真正复杂的现实任务需要异构的智能体系统。通过严格的理论推导，对比了单体学习者的优化约束与智能体系统的效率，从简单路由机制发展到通用有向无环图（DAG）拓扑结构。结果表明，智能体系统在泛化能力和样本效率上具有指数级优势。最后，论文探讨了其与专家混合模型（Mixture-of-Experts）的关联，重新解释当前多智能体框架的不稳定性，并呼吁加强对此方向的研究。
原文摘要 · Abstract (English)
Is monolithic scaling the only path to AGI? This paper challenges the dogma that purely scaling a single model is sufficient to achieve Artificial General Intelligence. Instead, we identify Agentic AI as a necessary paradigm for mastering the complex, heterogeneous distribution of real-world tasks. Through rigorous theoretical derivations, we contrast the optimization constraints of monolithic learners against the efficiency of Agentic systems, progressing from simple routing mechanisms to general Directed Acyclic Graph (DAG) topologies. We demonstrate that Agentic AI achieves exponentially superior generalization and sample efficiency. Finally, we discuss the connection to Mixture-of-Experts, reinterpret the instability of current multi-agent frameworks, and call for greater research focus on Agentic AI.
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