Abstract: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.
| Comments: | Accepted by ICML'26 Position Track |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.12966 [cs.AI] |
| (or arXiv:2605.12966v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12966 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Junwei Liao [view email]
[v1]
Wed, 13 May 2026 04:00:43 UTC (237 KB)