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Anthropic debuts AI-driven pharma R&D tool, Claude Science
Anthropic launches Claude Science as big tech companies increasingly eye expansion in the wider healthcare and life sciences space.
Claude Helped a Hacker Find a Way to Issue Tickets to Almost Every US Music Festival
A researcher found that using Anthropic’s Claude Opus 4.7, he could break into the website of Front Gate—used by every festival from Lollapalooza to Bonnaroo—and freely issue any ticket he chose.
Anthropic launches Claude Science for pharma researchers
Anthropic had made a big life sciences play with the launch of Claude Science, an AI 'workbench' for researchers involved in drug discovery.
Anthropic Launches Claude Science to Court Pharma Ahead of IPO
Anthropic unveiled Claude Science, an AI workbench designed to run — not just assist with — pharma research. The company is seeking to build out its customer base in the pharma sector as it deepens its healthcare push ahead of a widely anticipated IPO.
5 takeaways from Anthropic’s big science event
The company rolled out a biology-centric version of Claude and hosted surprisingly forthright conversations about AI and science.
Trump administration lifts Claude Mythos 5, Fable 5 export restrictions after Anthropic works with government
The Commerce Department lifted export restrictions on Anthropic's Claude Fable 5 and Mythos 5 after a review, clearing the models for wider access
Anthropic unveils drug discovery plans alongside Claude Science, an AI tool for drugmakers
At an event on Tuesday, the AI heavyweight said it plans to target "neglected" diseases.
Anthropic's New AI Workbench Mapped My Field For $26. Now Imagine It Aimed At The Rest Of Science
Anthropic's Claude Science is a capable AI research workbench built for molecular biology. The opportunities for the rest of science are enormous.
Claude Science is Here, Antibiotics Designed by Text Prompt Among Applications
Basecamp Research’s antibiotic design and vaccine prediction AI models are now available through Anthropic’s Claude Science.
Researchers say Anthropic’s Claude Science will boost drug discovery
Anthropic's Claude Science aims to speed up drug discovery. Northeastern scientists weigh in on what it could mean for their research.
Claude Science is Anthropic’s newest flagship product
The company is doubling down on AI for science.
Anthropic launches Claude Science
CNBC's Angelica Peebles to talk Anthropic's new healthcare offering.
With Claude Science, Anthropic Targets Another Application
Anthropic is targeting the science vertical with a new AI workbench.
Anthropic Launches Claude Science AI Workbench for Scientific Research
June 30, 2026 — AI has the potential to dramatically accelerate the pace of scientific discovery and the development of healthcare interventions. Since launching efforts in the life sciences last fall, […]
Anthropic launches AI drug discovery program, joining tech giants in betting on healthcare
Anthropic will start an internal drug discovery program, part of a new push to sell artificial intelligence tools to drugmakers.
Anthropic launches AI drug discovery program, joining tech giants in betting on healthcare
Anthropic will start an internal drug discovery program, part of a new push to sell artificial intelligence tools to drugmakers.
Anthropic debuts Claude Science, an AI product for bioscience
On Tuesday, Anthropic announced Claude Science, a new product that company leaders describe as an AI workbench for scientific research.
Anthropic just released a brand-new Claude Science app for Mac
Anthropic just launched a brand-new desktop app called Claude Science. The new app joins the main Claude app on the...
Anthropic unveils 'Claude Science' AI platform for scientific research
Anthropic on Tuesday launched Claude Science, an AI platform designed to help scientists streamline research, analyze data and manage complex computing workflows.
Anthropic’s Claude Science bets on workflow, not a new model, to win over scientists
Anthropic's Claude Science is a workbench that gives scientists one environment to do computational research, saving them from the need to bounce between databases, pipelines, and tools.
Anthropic releases Claude Science, a product aimed at researchers, the pharma industry
Anthropic released Claude Science, an application that optimizes its large language model for scientists and, especially, those doing research at pharma companies.
Anthropic launches Claude Science in bid to expand revenue streams ahead of IPO
Anthropic is launching its Claude Science product as it seeks to increase revenue streams ahead of its anticipated initial public offering.
The Briefing: AI for Science \ Anthropic
The Briefing is coming to San Francisco on June 30, 2026. Join Anthropic leadership, life sciences executives, and leading research institutions for an exclusive look at how Claude is being put to work across science.
Newsom's office touts Anthropic 'partnership,' 50% discount on Claude AI for California agencies, localities
Golden State Gov. Gavin Newsom's office indicated that state agencies and local governments may access Anthropic's Claude artificial intelligence platform at a 50% discount.
Austria lobbies EU to host Anthropic AI after US curbs, Bloomberg News reports
Austria is pushing the European Union to consider hosting Anthropic AI system within its borders to counter efforts by the U.S. to block foreigners from using the company's most advanced models, Bloomberg News reported on Sunday.
Anthropic's Mythos 5 gets limited carveout from US restrictions
Anthropic said it is restoring access to Mythos 5 for a set of US organizations that "operate and defend critical infrastructure."
US close to allowing Anthropic to restore Fable 5 model, Axios reports
The Trump administration is close to allowing Anthropic to restore access to its Fable 5 model, Axios reported on Saturday, citing a source close to the situation.
US government allows Anthropic limited release of AI model that sparked cybersecurity concerns
The US government has allowed Anthropic to release its powerful Mythos AI model to select companies and organizations, revising license requirements after ordering an export block earlier this month in the wake of national security fears.
How should European scientists respond to the US AI kill switch?
Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
Anthropic accused Alibaba of exploiting its AI models
Anthropic said the Chinese company interacted with Claude millions of times through 25,000 fraudulent accounts in recent months.
OpenAI triumphs in brutal $26M political fight against Anthropic
Bores quickly became the pawn between the two California AI companies.
Anthropic Veterans’ Startup Seeks to Help Scientists Develop Their Own AI
Mirendil raises $200 million from Andreessen Horowitz and Kleiner Perkins to make AI that can do the job of an AI engineer.
AI models that can take down governments and business months away, rare Five Eyes statement warns
Signal agencies in Australia, the US, the UK, New Zealand and Canada sound alarm after Trump blocks foreign nationals from Anthropic’s Fable AI model
AI regulation is a mess, and Anthropic is caught in the crosshairs
The latest spat between Anthropic and the government raises a broad concern among AI and safety researchers: There is no consistent framework for regulating AI.
The Market’s AI Fanfare Is Running Into a Harsh Political Reality
Anthropic’s hopes for a blockbuster IPO could depend as much on the ballot box as on investors.
Nobel-winning AlphaFold pioneer leaves Google DeepMind for Anthropic
John Jumper, a key member of the team at Google DeepMind for nearly a decade, is decamping for Anthropic.
‘He has to find a way to be friends’: The political fight behind Anthropic’s export controls
In an administration that rewards political deference, Anthropic’s CEO has repeatedly clashed with the White House.
Why everybody wants to work at Anthropic or OpenAI
Anthropic and OpenAI are the new top-end employers. We spoke to applicants, Big Tech workers, and job coaches about why they're so in demand.
French Twist: Trump Upends GOP Plans | WSJ Politics Newsletter for June 18
Plus, Kevin Warsh makes his debut as Fed chairman and Trump offers encouragement to Anthropic over access to its powerful AI models
Anthropic Is Bringing Together AI Design and Coding in Claude
New updates mean you should be able to go back and forth between coding and designing without interruptions.
Anthropic ships major Claude Design overhaul with design system imports, code round-trips, and a fix for its token-burning problem
Anthropic has overhauled Claude Design with brand-compliance controls, Claude Code integration, lower token usage and new enterprise app exports, positioning the AI tool as a serious platform for design-to-code workflows.
Anthropic CEO Dario Amodei throws shade at Sam Altman
Anthropic CEO Dario Amodei, who famously left OpenAI in 2020, says he's "at peace" on where things stand with his chief AI rival.
I was using Anthropic's Fable when it disappeared mid-project
A UK founder was mid project with Anthropic's Fable 5 model when a US government order halted foreign access. Luckily, he had a backup plan.
OpenAI Just Started Selling What Your Consulting Business Sells
OpenAI and Anthropic just launched their own AI consulting firms. Here is what independent advisors and small firms should do to keep their clients.
Anthropic’s Scare Tactics, SpaceX’s New Deal and Qualcomm’s Bargain | AI & Business for June 16
Plus, SpaceX’s Cursor deal, and AI struggles to replace stenographers
Inside the White House’s Newest AI Clash With Anthropic | WSJ Politics Newsletter for June 16
Plus, President Trump spent his 80th birthday finalizing the Iran agreement as he watched the UFC contest
Trump just found the worst way to regulate AI
The real danger in Trump’s crackdown on Anthropic’s newest AI model.
Your cheat sheet to Anthropic's White House drama
Anthropic is at the center of another showdown with the Trump administration, this time over the company's Fable 5 and Mythos 5 AI models.
The quiet, galactic ambitions of Cursor CEO Michael Truell
How the hottest AI coding company navigated its situationship with Anthropic and hitched its fate to Elon Musk's chaotic rocket.
Trump admin says Anthropic's 'recklessness' triggered export controls on latest AI models
Anthropic's Fable 5 AI model faced export controls after a senior administration official says the company's "recklessness" in addressing vulnerabilities damaged government trust.
Inside what led the White House to slap export controls on Anthropic
Tense calls between Anthropic's CEO and administration officials on Friday underscore how the White House is wrestling with advanced AI models.
The Fable 5 AI Model Just Went Dark. What the US Government’s Playbook Means for AI Companies—and Every Business Leader
Anthropic’s newest AI went dark just three days after launch. What happened next offers a stark warning for any CEO facing a powerful adversary.
Small Business Tech News This Week: Google, Ramp, Slack, Anthropic, Microsoft
Google is warning of fake IT workers, Payables management company Ramp unveils a new product, Slack has more workflow capabilities and more news.
Opinion | It's time for Anthropic to get out of the business of killing people
The AI giant won’t allow the military to use its technology for autonomous weapons. That’s a good step. But it’s not enough.
Trump Administration Reignites Its Feud With Anthropic Over Latest A.I. Models
The administration’s surprise restrictions Friday cut off foreign access to Anthropic’s latest models and sparked another round of finger pointing.
Big money is pouring into South Florida health systems. What it means for patients
Just weeks ago, Baptist Health South Florida announced the largest single philanthropic gift in the organization’s 66-year history.
US Government Suspends Access To Claude Fable 5 And Mythos 5 (What It Means For Your Business)
The US government just forced Anthropic to suspend Claude Fable 5 and Mythos 5 overnight. Here is what every business owner building on AI needs to do.
Tech world reacts to Trump controls on Anthropic's Fable and Mythos
One CEO said the move by the Trump administration and the response by Anthropic would 'send shockwaves' across AI labs.
Why Anthropic freaked out the AI industry this week
Anthropic limits AI model use, citing safety concerns. But it's also trying to stop open-source models from competing.
A Price War Is Brewing Between OpenAI and Anthropic — And It Could Make AI a Lot Cheaper for Your Business
OpenAI is weighing drastic cuts to what it charges for AI tokens. It expects Anthropic to do the same.
Is Vibe Coding Already Dead? Even Karpathy Is Moving On
Andrej Karpathy declared vibe coding passe in February and joined Anthropic in May. Here is what agentic engineering changes for founders this quarter.
Anthropic Blindsides Its Business Partners
Weeks before Anthropic in April revealed Claude Design, an AI tool for creating designs and software application prototypes, it asked firms including Figma and Canva to be “partners” of the launch announcement showcasing the tool’s capabilities. These two design firms, longtime Anthropic ...
Businesses feeling the pain from AI spending, so OpenAI reportedly weighs price cuts
An artificial intelligence price war between OpenAI and Anthropic might be brewing, with The Wall Street Journal reporting that OpenAI, the maker of ChatGPT, is considering "drastic" price cuts for business clients.
As OpenAI leans into enterprise business, Apple and Google set sights on the masses
OpenAI's aggressive push into the enterprise, where it competes with Anthropic, coincides with efforts by Apple and Google to bring advanced AI to consumers.
OpenAI and Anthropic keep warning about a future they're building
Both Anthropic and OpenAI have released papers warning that frontier models are being deployed before government regulation can catch up.
Anthropic says 'we made the wrong tradeoff' in new model guardrails
"We're changing Fable 5's safeguards for frontier LLM development to make them visible," an Anthropic spokesperson said.
Anthropic's Amodei advocates for AI-related job displacement plan
Anthropic CEO Dario Amodei warns AI could cause long-term job loss, urging policies like universal basic income to mitigate its impact.
Researchers are furious over Anthropic's hidden AI limits
Anthropic faces backlash as Mythos-based models intentionally limit help for AI research, raising transparency and ethical concerns.
Microsoft AI head calls out Anthropic for acting like Claude is conscious
“This is exactly what we don’t want from AIs.”
Perplexity stays course on 2028 IPO amid OpenAI, Anthropic IPOs
Investors are closely watching whether OpenAI and Anthropic can justify their lofty valuations.
New Oregon State building will support five field sports
Oregon State University supporters have made philanthropic commitments totaling $25 million to construct a new facility that will support 150 student-athletes in softball, men’s and women’s soccer, women’s cross country an
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
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