EvoTreeNAD is a genealogy‑guided evolutionary algorithm that autonomously discovers neural architectures without a predefined seed or search space. Starting from an empty root, it builds a persistent genealogy where each node represents a complete architecture; top‑percentile values from nodes and descendants steer lineage selection. The method combines an Idea Agent that proposes variants and a Code Agent that implements them, with theoretical analysis showing stationary variation regimes and empirical results demonstrating superior performance on CIFAR‑10/100 and MedMNIST‑v2 tasks.
By Lishan Yu, Derek Jiu, Qizhen Lan, Xiaoqian Jiang
arXiv:2607. 07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task.
By Seokhoon Jeong, Mijung Kim, Taehwan Kim
arXiv:2606. 26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed.
By Boyun Zhang, Chao Wang, Kai Wu
arXiv:2606. 13710v1 Announce Type: new Abstract: Deep research and agent evolution serve as de-facto tasks for AI agents in real-world applications toward artificial general intelligence.
By Hongming Piao, Chi Liu, Mengzhuo Chen, Yan Shu, Derek Li, Ying Wei, Bryan Dai
The paper proposes a framework for building autonomous AI research teams that can discover linear attention architectures. By assembling agents with diverse disciplinary expertise, private research histories, shared experimental evidence, and adaptive allocation of research opportunities, the team can balance exploration and exploitation under limited experimental budgets. Experiments show that this design outperforms GDN2 at 1.3B scale on language modeling, common‑sense reasoning, and retrieval tasks, and that removing any single component weakens discovery.
By Yujian Liu, Li An, Zelin He, Jacob Helwig, Jiabao Ji, Rohit Jain, Siyu Zhu, Shiyu Chang
We study what happens when a single general-purpose large language model acts as the sole researcher on a long-horizon neural architecture design problem. The agent receives a scientific question, an initial hypothesis and motivation, a compute budget, and research affordances (source and experiment management, experiment tracking, literature access, and persistent memory), then autonomously proposes, implements, evaluates, and records experiments over an extended period.