Good scientists reveal how they do their experiments and report their results; so should any machine-driven research
Verification in the Age of AI Scientists
AI Scientists now operate at a scale that outpaces human capacity for manual review. Systems such as Sakana’s AI Scientist write entire workshop papers end-to-end. Lila Sciences runs autonomous ``AI Science Factories’’ that hypothesize, experiment, and iterate without human guidance. FutureHouse’s Kosmos and Robin generate thousands of candidate hypotheses in a single run, and Google’s Co-Scientist proposes testable experiments at a rate no laboratory can fully evaluate. Each of these systems emphasizes verified results, yet that standard ranges from near-perfect formal proof in mathematics to decade-long clinical trials in medicine, with no shared framework for judging sufficiency across domains. As outputs scale beyond what humans can manually inspect, the question of which results to trust becomes as hard as generating them. The bottleneck for AI for Science is no longer hypothesis generation, it is verification.
Bending the Curve: How AI is Transforming Scientific Discovery
Discover how researchers use LLMs and specialized AI models like AlphaFold as complementary tools to accelerate innovation and expand the scientific frontier.
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.
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.
No. 022: Palomar proof registry, autonomous protein design, synthetic consensus - The Hallway Track
The Hallway Track No. 022 - How AI is affecting the practice of science
AI, peer review and the human activity of science
When researchers cede their scientific judgement to machines, we lose something important.
Konrad Hinsen's blog
The advent of AI agents based on large language models (LLMs) has put the idea of automating the intellectual and cognitive work of researchers on the table. A lively, sometimes even heated discussion is already going on. A frequently missing piece in this debate is the question why we, individually and as a society, actually do science. I will examine this question first, and then consider what it implies for introducing automation into science.
Where does the rigor go? Research software and the future of trustworthy science.
Generative AI now makes it dramatically easier to produce something that looks like research: analysis code, figures, literature reviews, even whole pap…
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?
Dan Shipper 📧 on Twitter / X
this is true and is a big reason why you don’t need to be a highly technical researcher to use LLMs in surprising and novel ways https://t.co/TuxNzXzToU— Dan Shipper 📧 (@danshipper) July 27, 2025
Jan-Nano : 1st Deep Research LLM
From Genius Science to Scenius Science - Cosmik Labs
Why personal AI tools might not be all we need to revolutionize science
KillBench: Discovering Hidden Biases of LLMs
1M+ experiments exposing bias in critical AI decision-making
Deep Research, information vs. insight, and the nature of science
What AI will accelerate in the scientific process, what it cannot do, and how we can prepare for new manners of scientific investigation.
AI for science: What can it do? Can it do things? Let’s find out!
A what we’re reading spotlight
AI for Research | Scite
Scite searches 280M+ scholarly articles to give you citation-backed answers and show you how every claim is supported or disputed.