The Download: Anthropic launches Claude Science, and California's carbon manure math Plus: The US has lifted restrictions on Anthropic's Mythos and Fable models. Claude Science is Anthropic's newest flagship product At an event for pharmaceutical executives, biotech founders, and researchers yesterday, Anthropic announced Claude Science, a major new product intended to support scientific research like Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work from concise, high-level instructions, with tools for computational biology and drug development. The launch signals that Anthropic is doubling down on AI for science, and the company will also use the product in its own research into drugs for rare, neglected diseases. Discover why Anthropic is betting big on AI for scientific research . Why California's carbon manure math doesn't add up Years ago, the state set up a system that pays cattle farmers to turn the methane emitted from cattle manure into natural gas.

At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access to tools that make it particularly useful for research in computational biology and drug development. Along with launching and previewing Claude Science, which is now available to all paid Claude subscribers, Anthropic also announced that it will be using the product to pursue some of its own research into drugs for rare, neglected diseases. This is not Anthropic's first foray into AI for science. In October, the company released plug-ins that help Claude make use of scientific software and databases under the heading "Claude for Life Sciences." But unlike this earlier release, Claude Science is a full-featured, standalone product. Anthropic's decision to elevate Claude Science to the same rank as Claude Code and Claude Cowork indicates that the company is taking AI's scientific applications very seriously--or at least wants to give the impression that it is.

Apple could unveil Gemini-powered Siri in Feb. Prism can edit and format LaTeX. OpenAI is releasing a new app called Prism today, and it hopes it does for science what coding agents like Claude Code and its own Codex platform have done for programming. Prism builds on Crixet, a cloud-based LaTeX platform the company is announcing it acquired today. For the uninitiated, LaTeX is a typesetting system for formatting scientific documents and journals. Nearly the entire scientific community relies on LaTeX, but it can make some tasks, such as drawing diagrams through TikZ commands, time-consuming to do.

America's new dietary guidelines ignore decades of scientific research An emphasis on fruit, vegetables, and whole foods is welcome--but it's wrong to suggest steak and beef tallow should be prominent. The new year has barely begun, but the first days of 2026 have brought big news for health. On Monday, the US's federal health agency upended its recommendations for routine childhood vaccinations--a move that health associations worry puts children at unnecessary risk of preventable disease. There was more news from the federal government on Wednesday, when health secretary Robert F. Kennedy Jr. and his colleagues at the Departments of Health and Human Services and Agriculture unveiled new dietary guidelines for Americans . And they are causing a bit of a stir. RFK Jr's plan to improve America's diet is missing the point That's partly because they recommend products like red meat, butter, and beef tallow--foods that have been linked to cardiovascular disease, and that nutrition experts have been recommending people in their diets.

In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical. Existing evaluation datasets for this task are limited, primarily due to the high costs and effort required to annotate resources that effectively represent complex queries. To address this, we propose a novel task, $\textbf{S}$cientific $\textbf{Do}$cument $\textbf{R}$etrieval using $\textbf{M}$ulti-level $\textbf{A}$spect-based qu$\textbf{E}$ries (DORIS-MAE), which is designed to handle the complex nature of user queries in scientific research. We developed a benchmark dataset within the field of computer science, consisting of 100 human-authored complex query cases. For each complex query, we assembled a collection of 100 relevant documents and produced annotated relevance scores for ranking them.

Frontier AI agents show increasing promise as scientific research assistants, and may eventually be useful for extended, open-ended research workflows. However, in order to use agents for novel research, we must first assess the underlying faithfulness and correctness of their work. To evaluate agents as research assistants, we introduce ReplicationBench, an evaluation framework that tests whether agents can replicate entire research papers drawn from the astrophysics literature. Astrophysics, where research relies heavily on archival data and computational study while requiring little real-world experimentation, is a particularly useful testbed for AI agents in scientific research. We split each paper into tasks which require agents to replicate the paper's core contributions, including the experimental setup, derivations, data analysis, and codebase. Each task is co-developed with the original paper authors and targets a key scientific result, enabling objective evaluation of both faithfulness (adherence to original methods) and correctness (technical accuracy of results). ReplicationBench is extremely challenging for current frontier language models: even the best-performing language models score under 20%. We analyze ReplicationBench trajectories in collaboration with domain experts and find a rich, diverse set of failure modes for agents in scientific research. ReplicationBench establishes the first benchmark of paper-scale, expert-validated astrophysics research tasks, reveals insights about agent performance generalizable to other domains of data-driven science, and provides a scalable framework for measuring AI agents' reliability in scientific research.

Breakthroughs, discoveries, and DIY tips sent every weekday. Humans have a long history with alcohol--we've been making and consuming it for over ten thousand years, about as long as we've had agriculture. That's a long time for people to come up with all kinds of ideas about the drug and how it works. So, not surprisingly, some of them are wrong. Here are a few common myths about alcohol, debunked by scientific research.

Large Language Models (LLMs) play a central role in modern artificial intelligence, yet their development has been primarily focused on English, resulting in limited support for other languages. We present PLLuM (Polish Large Language Model), the largest open-source family of foundation models tailored specifically for the Polish language. Developed by a consortium of major Polish research institutions, PLLuM addresses the need for high-quality, transparent, and culturally relevant language models beyond the English-centric commercial landscape. We describe the development process, including the construction of a new 140-billion-token Polish text corpus for pre-training, a 77k custom instructions dataset, and a 100k preference optimization dataset. A key component is a Responsible AI framework that incorporates strict data governance and a hybrid module for output correction and safety filtering. We detail the models' architecture, training procedures, and alignment techniques for both base and instruction-tuned variants, and demonstrate their utility in a downstream task within public administration. By releasing these models publicly, PLLuM aims to foster open research and strengthen sovereign AI technologies in Poland.

MIT astronauts aboard the International Space Station--and the MIT researchers who have sent up experiments--have advanced our understanding of science, space, and the universe. This image of the International Space Station and space shuttle Endeavour, flying at an altitude of approximately 350 kilometers, was taken by Expedition 27 crew member Paolo Nespoli from the Soyuz TMA-20 on May 24, 2011. On November 2, 2000, NASA astronaut Bill Shepherd, OCE '78, SM '78, and Russian cosmonauts Sergei Krikalev and Yuri Gidzenko made history as their Soyuz spacecraft docked with the International Space Station. The event marked the start of 25 years of continuous human presence in space aboard the ISS--a prolific period for space research. MIT-trained astronauts, scientists, and engineers have played integral roles in all aspects of the station's design, assembly, operations, and scientific research. One of MIT's most experienced NASA astronauts, Mike Fincke '89, is celebrating that milestone from space.

We introduce PETLP (Privacy-by-design Extract, Transform, Load, and Present), a compliance framework that embeds legal safeguards directly into extended ETL pipelines. Central to PETLP is treating Data Protection Impact Assessments as living documents that evolve from preregistration through dissemination. Through systematic Red-dit analysis, we demonstrate how extraction rights fundamentally differ between qualifying research organisations (who can invoke DSM Article 3 to override platform restrictions) and commercial entities (bound by terms of service), whilst GDPR obligations apply universally. We demonstrate why true anonymisation remains unachievable for social media data and expose the legal gap between permitted dataset creation and uncertain model distribution. By structuring compliance decisions into practical workflows and simplifying institutional data management plans, PETLP enables researchers to navigate regulatory complexity with confidence, bridging the gap between legal requirements and research practice.