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.
arXiv:2602. 18008v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in constructing mechanistic models from data.
By Zihan Guan, Rituparna Datta, Mengxuan Hu, Shunshun Liu, Aiying Zhang, Prasanna Balachandran, Sheng Li, Anil Vullikanti
The paper introduces PACEvolve, a framework that improves self‑evolving agents powered by Large Language Models by addressing their tendency to become trapped in local contexts and repeat flawed hypotheses. It does so through three techniques: Hierarchical Context Management to prune memory, Momentum‑Based Backtracking to escape local minima, and a self‑adaptive Collaborative Evolution policy to balance refinement and knowledge transfer. These methods enable the agents to maintain a global view of search momentum and achieve state‑of‑the‑art results on complex evolutionary benchmarks.
By Minghao Yan, Bo Peng, Benjamin Coleman, Ziqi Chen, Zhouhang Xie, Shuo Chen, Zhankui He, Noveen Sachdeva, Isabella Ye, Weili Wang, Chi Wang, Ed H. Chi, Fernando Pereira, Wang-Cheng Kang, Derek Zhiyuan Cheng, Beidou Wang
EPOCH is an evidence‑governed architecture for AI research agents that improves how candidate discoveries are evaluated and validated. By combining explicit task contracts, typed memory, active falsification, admission checks, and independent replay, EPOCH ensures that each candidate is judged against the strength and scope of its claim. The system achieves state‑of‑the‑art performance on several benchmarks, including AlgoTune, Math14, and AgentHPO, and demonstrates significant advances in executable constructions, algorithm optimization, counterexamples, and proof‑supported results across ten discovery problems.
By Binjie Guo, Aisheng Mo, Ruitong Li, Xinle Deng
Strategist is a meta‑agent that automatically evolves inference strategies for AI agents, turning model calls into responses. It stores reusable strategy blueprints in a library, retrieves relevant ones for new tasks, generates and tests candidate strategies, and refines them based on results. Across 30 benchmarks, Strategist outperforms the strongest baseline on 24 tasks, matches its average accuracy while cutting inference cost by 39%, and its strategies transfer to unseen tasks and models without additional search.
By Taiming Lu, Qi Xu, Zhigang Hua, Zhuang Liu
arXiv:2607. 06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling, extended chain-of-thought), or benchmark-specific training in which small models are fine-tuned on ARC data, often with task-specialized architectures.
By Kabir Moghe, Peter Chin
The paper introduces a semi-automated method for structuring neural architecture search (NAS) using large language models (LLMs). It defines a high-level template of architectural attributes, which an LLM populates by analyzing research papers to create a diverse search space. The authors then propose FairNAD, a multi-type mutation strategy that combines fair idea sampling, Pareto-aware mutation, LLM-driven iterative mutation, and a fine-grained feedback loop to efficiently explore this space, achieving significant performance gains on CIFAR-10, CIFAR-100, and ImageNet16-120.
By Yuiko Sakuma, Masakazu Yoshimura, Marcel Gr\"opl, Zitang Sun, Junji Otsuka, Atsushi Irie, Takeshi Ohashi