Data to 7 Oct 2026

Position: Agentic AI System Is a Foreseeable Pathway to AGI

arXiv 2605.12966 ↗First posted 13 May 2026cs.AIcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 22 of its 41 references that are in the map. First posted on arXiv 13 May 2026.

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Other references · 19

2020Scaling Laws for Neural Language Modelscited by 6,411
2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
2018Relational inductive biases, deep learning, and graph networkscited by 1,341
2016On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minimacited by 1,337
2020Gradient Surgery for Multi-Task Learningcited by 1,210
2023GAIA: a benchmark for General AI Assistantscited by 1,084
2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 441
2019Are Transformers universal approximators of sequence-to-sequence functions?cited by 404
2014Identifying and attacking the saddle point problem in high-dimensional non-convex optimizationcited by 286

7 more

2025Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systemscited by 170
2024A Tale of Tails: Model Collapse as a Change of Scaling Lawscited by 127
2024Resolving Discrepancies in Compute-Optimal Scaling of Language Modelscited by 84
2024Reconciling Kaplan and Chinchilla Scaling Lawscited by 36
2023Approximation Rate of the Transformer Architecture for Sequence Modelingcited by 28
2024Proactive Gradient Conflict Mitigation in Multi-Task Learning: A Sparse Training Perspectivecited by 12
2022Upper bounds on the Natarajan dimensions of some function classescited by 8

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