New preprint: AIPOCH Open-Science: An Open-Source AI Research Workbench with Reproducible .science Packages. Paper: arxiv.org/abs/2610.17110 GitHub: github.com/aipoch/open-scien… When AI-assisted research changes hands, can the next researcher tell what ran, which output was reviewed, and what remains unverified? AIPOCH Open-Science brings researcher-directed work into a local-first, open-source workspace. Its portable .science packages carry conversation history, computations, artifact versions and recorded checks together. The paper examines how these relationships survive export and import—even when an output is revised or eligible files are omitted. Reviews stay tied to the versions they assessed; an unchecked revision does not inherit an earlier review. Imported history remains read-only, with a separate writable continuation. In a controlled same-installation study, complete and partial exports preserved the tested version and review relationships. A separate offline rerun, using a retained script and fixed input, reproduced two saved CSV tables byte for byte. These results test evidence preservation and a bounded rerun, not the scientific validity of every result. The preprint documents the mechanisms and their limits, with public code and validation materials.

We want to support the mathematics community in turning progress in AI into deeper human understanding and new discoveries. To support that goal, we are contributing $5 million to a new Simons Foundation initiative to help mathematicians examine, understand, and build on results produced by AI, including the results we recently published. The funding, managed and awarded by the @SimonsFdn will support workshops, conferences, and special programs. We will also provide access to our models and computing resources to Simons Foundation-supported researchers.

We're expanding access to GPT-Rosalind for academic research. The new Rosalind Academic Plan helps scientific labs bring AI into the work of understanding biology, exploring ideas, and deciding what to test next—with sponsored access for eligible labs at U.S. universities. Academic research starts with questions whose impact may take years to impact human health.

In physics, an “impedance mismatch” occurs when two systems each work well but are poorly matched. In this Science Blog guest post, Harvard physicist Matthew Schwartz argues that something similar is happening with AI and science. LLMs are capable at many things, but working with them as you would with a human collaborator isn’t currently the best way to elicit their scientific strengths. To address this mismatch, Schwartz created a toolkit for exact calculations in quantitative science. Because similar calculations often emerge in very different areas of science, Claude found connections to ecology, population genetics, and a dozen other fields, and Schwartz worked with domain experts to steer it towards interesting questions. Read more about these projects here: anthropic.com/research/claud…

We're deepening our commitment to scientific research and technological discovery by committing $150 million to the Genesis Mission. We'll also make Claude and our technical support available to more than 15 federal agencies. anthropic.com/news/genesis-m…

An astrophysicist worked with Claude Science to create the first complete ultraviolet map of the sky. Astronomers have already created complete sky maps from radio to gamma rays. But large regions of the sky have never been observed in UV. In today’s science blog, Brice Ménard explains how he guided Claude to find existing datasets, combine them, and fill in the gaps with statistical inference. The work would have taken humans weeks of work, but, with Claude, just took a few days while Ménard worked on other projects. The resulting map is a useful teaching tool and an example of exactly the sort of slow, low-priority work which scientists never quite get around to, but which AI now makes feasible. Read more: anthropic.com/research/the-m…

Working out how best to use AI in mathematics research—and in science more generally—will take time, effort, and resources. We’re eager to support mathematicians as they drive scientific progress and define the future of their field. We’ve committed $5 million of funding, compute, and Claude access to help the math community navigate the massive changes that AI brings. We’re partnering with @SimonsFdn, who will manage the award through a community-driven approach, supporting workshops, conferences, and lectures by domain experts. xitter.cf/SimonsFdn/status/21107…

Join the webinar on October 22nd to explore the agent engineering patterns needed to make research workflows more accurate, observable, and reliable.

Gemini 4 Argon is already unlocking new scientific breakthroughs in quantum computing, nuclear fusion, protein design and beyond. 🧵

The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Blog: pub.sakana.ai/agentic-scient… GitHub: github.com/SakanaAI/AI-Scien… arXiv: arxiv.org/abs/2610.18933 Since 2024, we’ve used The AI Scientist as a way to test how far AI alone can take scientific research, from v1 to v2 to the paper published in Nature earlier this year. The Agentic Scientist is the next step in that line of work, built for the era of coding agents. Coding agents such as Codex and Claude Code now carry out long software tasks on their own. Handing them a research problem, however, tends to produce a strong hyperparameter optimizer rather than a scientist: scores go up, but ideas stay narrow, and the reasons for the gain are rarely examined. The Agentic Scientist makes the research loop explicit. It wraps off-the-shelf coding agents in a scaffold that controls what they see, what they write, how they are evaluated, and how results are handed to the next stage. Parallel workers test hypotheses against rubrics defined before any experiment runs, share successes and failures through a common memory, and then ablate the winning program to generate evidence. We used an early version of the system to study Error Diffusion, a biologically motivated alternative to backpropagation. Our researchers set the question and the rubric, chose directions at each stage, and verified the results, references, and claims before submission. The resulting paper was accepted as an oral presentation at ALIFE 2026. In autonomous runs, the system also raised Doc-to-LoRA retrieval accuracy from 52% to 98% on multi-needle tasks and designed a learned evolution strategy that improves over its CMA-ES reference on unseen tasks. Alongside it we release AutoIllustrate, a pipeline for scientific figures that keeps an editable draw.io file as the source of truth, so every diagram can be corrected by hand. Humans define the question and the standards of evidence; agents expand the experiments researchers can run. We call this Co-Discovery, and we are releasing both systems as open source for the community to build on.