Nature - State-of-the-industry report finds that, despite the limitations of artificial-intelligence systems, researchers have embraced them.
Science that Compounds: The Need for A New Substrate for Research in the Age of AI
This paper is a perspective from Lightcone Research, an open-source initiative building tooling for scientific research in the age of agentic AI.
I don’t think we are close to “AI scientists”
Today's AI agents are not designed to extract deep insights from new observations.
Introduction to Agents
Discover what actually works in AI. Join millions of builders, researchers, and labs evaluating agents, models, and frontier technology through crowdsourced benchmarks, competitions, and hackathons.
Agents
Intelligent agents are considered by many to be the ultimate goal of AI. The classic book by Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Prentice Hall, 1995), defines the field of AI research as “the study and design of rational agents.”
AI co-scientists are revolutionizing how research is done
Artificial-intelligence systems can generate hypotheses, design experiments and analyse data — but humans still need to decide what makes sense.
AI is An Ideology, Not A Technology
At its core, "artificial intelligence" is a perilous belief that fails to recognize the agency of humans.
The 2025 AI Agent Index
Agentic AI systems are increasingly capable of performing complex tasks with limited human involvement. The 2025 AI Agent Index documents the origins, design, capabilities, ecosystem, and safety features of 30 prominent AI agents based on publicly available information and correspondence with developers.
AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions
Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.
AI for science needs reasoning, not just data
AI agents that can model the human process of research will accelerate discoveries in science.
AI agents team up in Agent Laboratory to speed scientific research
Johns Hopkins University and AMD have developed Agent Laboratory, a new open-source framework that pairs human creativity with AI-powered workflows.
AI agents pose untold risk to humanity. We must act to prevent that future | David Krueger
The pieces are falling into place for autonomous artificial intelligence. We must stop unregulated development
From Genius Science to Scenius Science - Cosmik Labs
Why personal AI tools might not be all we need to revolutionize science
Which ‘AI scientist’ suits your lab? A guide for the perplexed
General-purpose AI tools for science, such as Claude Science, promise to accelerate research. But which is right for you?
How to build an AI scientist: first peer-reviewed paper spills the secrets
AI Scientist, an autonomous research tool, first released in 2024, has now undergone peer review, highlighting its strengths and limitations.
AI for science: What can it do? Can it do things? Let’s find out!
A what we’re reading spotlight
