30 citations · 55 across the 10 of their papers we have counts for
10 papers
Towards End-to-End Automation of AI Research
Yutaro Yamada, Robert Tjarko Lange, Cong Lu +5
The automation of science is a long-standing ambition in the field of AI. While the community has made significant progress in automating individual components of the scientific pr…
Learning to Continually Learn via Meta-learning Agentic Memory Designs
Yiming Xiong, Shengran Hu, Jeff Clune
The statelessness of foundation models bottlenecks agentic systems' ability to continually learn, a core capability for long-horizon reasoning and adaptation. To address this limit…
Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
Jenny Zhang, Shengran Hu, Cong Lu +2
Today's AI systems have human-designed, fixed architectures and cannot autonomously and continuously improve themselves. The advance of AI could itself be automated. If done safely…
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
Yutaro Yamada, Robert Tjarko Lange, Cong Lu +5
AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing t…
Automated Capability Discovery via Foundation Model Self-Exploration
Cong Lu, Shengran Hu, Jeff Clune
Foundation models have become general-purpose assistants, exhibiting diverse capabilities across numerous domains through training on web-scale data. It remains challenging to prec…
Automated Design of Agentic Systems
Shengran Hu, Cong Lu, Jeff Clune
Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thou…
Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models
Cong Lu, Shengran Hu, Jeff Clune
Go-Explore is a powerful family of algorithms designed to solve hard-exploration problems built on the principle of archiving discovered states, and iteratively returning to and ex…
Thought Cloning: Learning to Think while Acting by Imitating Human Thinking
Shengran Hu, Jeff Clune
Language is often considered a key aspect of human thinking, providing us with exceptional abilities to generalize, explore, plan, replan, and adapt to new situations. However, Rei…
Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation
Shengran Hu, Ran Cheng, Cheng He +3
For the goal of automated design of high-performance deep convolutional neural networks (CNNs), Neural Architecture Search (NAS) methodology is becoming increasingly important for…
Multi-objective Neural Architecture Search with Almost No Training
Shengran Hu, Ran Cheng, Cheng He +1
In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries. Despite the steady stream of impressive empirical results…