Zhijing Jin on Twitter / X

EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights

When Isaac Newton discovered the law of gravitation, he did so through an iterative process of analyzing observed data such as planetary patterns, finding the underlying mechanisms by describing patterns in mathematical equations, and refining his theory against the Moon's orbit, revealing the startling insight that the same force governs both falling apples and orbiting planets. Would it be possible for AI agents to make similar discoveries? To measure this ability, we introduce EurekaBench, a cross-domain benchmark that tests AI agents' ability to conduct long-horizon experiments and discover mechanisms that explain observations. We evaluate these mechanisms by the scientific insights that can be derived from them. EurekaBench contains an expert-verified set of 26 long-horizon tasks across neuroscience, computer science, chemistry, astrophysics, geophysics, and plasma physics, with a total of 306 scientific insights that the discovered mechanisms are expected to support. Our evaluation framework tests three axes of scientific discovery: agents' ability to follow known scientific constraints, the predictive accuracy of the discovered mechanisms, and whether these mechanisms yield scientific insights or inform future research. Our results show that current AI agents often overly fixate on predictive accuracy optimization, surpassing human scientists, while falling substantially short in deriving scientific insights.

Kevin Weil 🇺🇸 on Twitter / X

💥 Today we’re introducing Prism—a free, AI-native workspace for scientists to write and collaborate on research, powered by GPT-5.2.Accelerating science requires progress on two fronts:1. Frontier AI models that use scientific tools and can tackle the hardest problems2.… pic.twitter.com/cnLysHixuQ— Kevin Weil 🇺🇸 (@kevinweil) January 27, 2026

We're releasing Terminal-Bench-Science: a benchmark for evaluating AI agents on research workflows across scientific domains. 🚀 An ongoing community effort out of Stanford University & Laude… | Steven Dillmann | 13 comments

We're releasing Terminal-Bench-Science: a benchmark for evaluating AI agents on research workflows across scientific domains. 🚀 An ongoing community effort out of Stanford University & Laude Institute, built by the team behind Terminal-Bench together with scientific domain experts at research institutions worldwide. 🧪 ⚛️ 🌍 ➕ ⚙️ Terminal-Bench-Science 0.1 has 70 tasks across the life, physical, earth, mathematical, and engineering sciences. Each one is contributed by a researcher porting their own workflows into challenging agentic tasks, on problems they actually care about. 🧠 Anthropic's Opus 5 dominates most scientific domains with an overall pass rate of ~30%, except the mathematical sciences, where Fable 5 and OpenAI's GPT-5.6 Sol take the lead. SpaceXAI's Grok 4.6 is one of the best models for engineering tasks, Z.ai's GLM 5.3 is the best open model, and Kimi (Moonshot AI)'s Kimi K3 is the only model beyond Opus 5 that lands on both cost and token efficiency Pareto frontiers. 🏆 All leaderboard results are live here: https://lnkd.in/gYh99UfU 💻 You can find all tasks on our Github: https://lnkd.in/gejKmXpj Special thanks to our amazing reviewer team Allen Hart, Ivan Bercovich, Joe Janssen, Jiaming H., Steffen Bollmann, Sergey Aganezov, Ben Blaiszik, Karl Krauth, Aaron Feller, Ben Blaiszik, Yiqi Liu, Han Hou, Robert Joseph G., Robert Zhang; our AI research advisors Ryan Marten, Alex Shaw, Lin Shi, Ben Feuer, Mike Merrill, Gian Segato, Jenia Jitsev, Bodhisattwa Majumder, Peter Clark, Thomas Wolf, Braden Hancock, Andy Konwinski, Alex Dimakis; and especially to our co-leads Sanmi Koyejo and Ludwig Schmidt. | 13 comments on LinkedIn

AGENTWORLD | *

An exploration of planetary intelligence, tracing how computation and cosmology—from ancient mechanisms to AI—reshape Earth and its possible futures.

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.

Stargazer: A Scalable Model-Fitting Benchmark Environment for AI Agents under Astrophysical Constraints

The rise of autonomous AI agents suggests that dynamic benchmark environments with built-in feedback on scientifically grounded tasks are needed to evaluate the capabilities of these agents in research work. We introduce Stargazer, a scalable environment for evaluating AI agents on dynamic, iterative physics-grounded model-fitting tasks using inference on radial-velocity (RV) time series data. Stargazer comprises 120 tasks across three difficulty tiers, including 20 real archival cases, covering diverse scenarios ranging from high-SNR single-planet systems to complex multi-planetary configurations requiring involved low-SNR analysis. Our evaluation of eight frontier agents reveals a gap between numerical optimization and adherence to physical constraints: although agents often achieve a good statistical fit, they frequently fail to recover correct physical system parameters, a limitation that persists even when agents are equipped with vanilla skills. Furthermore, increasing test-time compute yields only marginal gains, with excessive token usage often reflecting recursive failure loops rather than meaningful exploration. Stargazer presents an opportunity to train, evaluate, scaffold, and scale strategies on a model-fitting problem of practical research relevance today. Our methodology to design a simulation-driven environment for AI agents presumably generalizes to many other model-fitting problems across scientific domains. Source code and the project website are available at https://github.com/Gudmorning2025/Stargazer and https://gudmorning2025.github.io/Stargazer, respectively.

AI for science: What can it do? Can it do things? Let’s find out!

A what we’re reading spotlight

Kosmos: An AI Scientist for Autonomous Discovery

Today, we are announcing Kosmos, our next-generation AI Scientist. Kosmos is a major upgrade on Robin, our previous AI Scientist. You can read about it in our technical report, here. Kosmos is available to use from day one on our platform, here.

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.

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.

No. 009: AI agents p-hacking, submission surge data, Gemini for Science - The Hallway Track

The Hallway Track No. 009 - How AI is affecting the practice of science

I don’t think we are close to “AI scientists”

Today's AI agents are not designed to extract deep insights from new observations.

Chinese Academy of Sciences unveils AI model system for scientific research

Researcher Yang Yanchu at the Aerospace Information Research Institute of the Chinese Academy of Sciences (CAS) reveals an AI model in Beijing, capital of China, April 28, 2026.

Human scientists trounce the best AI agents on complex tasks

State-of-the-industry report finds that, despite the limitations of artificial-intelligence systems, researchers have embraced them.

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.

Science Works on Twitter / X

London AI × Science Hackathon 👾Science Works × @inherent_labs • Oct 16-18Lots of scientific software is outdated, hard to use, and isn't interoperable – let alone making use of (or ready to interface with) AI.Or it's expensive. Join us to build better for the community pic.twitter.com/42Qq528DDw— Science Works (@sci_works) September 30, 2026