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20232026
most citedThe AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

114 citations · 159 across the 13 of their papers we have counts for

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8 papers · 1 filter

cs.AI2026

AI Finds A Way

Aaron Dharna, Cong Lu, Ryan Sullivan +3

Artificial Intelligence (AI) algorithms frequently learn creative and unexpected solutions, surprising even expert researchers who develop and study them. They often astonish pract…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2025

SIMA 2: A Generalist Embodied Agent for Virtual Worlds

SIMA team, Adrian Bolton, Alexander Lerchner +63

We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…

cs.AI2025★ 6 cited

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…

cs.AI2025★ 30 cited

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…

cs.AI2024★ 114 cited

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Chris Lu, Cong Lu, Robert Tjarko Lange +3

One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models h…

cs.AI2024★ 8 cited

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…