1. Cal Newport tells the AI labs to cut the doom trolling

2. SecureBio puts its AI biology scores on one dashboard

3. RAND’s pitch for a cryptographic paper trail on AI-designed biology

4. The anti-scaling law and AI’s crowding problem in drug discovery

5. Local models are finally good enough for real work

6. Heng Li’s minibwa replaces BWA-MEM

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1. Cal Newport wants the doom trolling to stop

I have read Cal Newport for years, going back to Deep Work, which I recommend to anyone who says they can’t find time to think. So I came to his new NYT op-ed already sympathetic: Dear A.I. Companies: The Doom Trolling Needs to Stop. His target is what he calls doom trolling: labs cataloging the catastrophes their models might cause while insisting they are powerless to stop building them. He points at Anthropic’s “When AI builds itself” report and at Sam Altman’s posting images of the Death Star before announcing GPT-5.

Cal’s argument: either the labs believe the risk is real, in which case the only defensible move is to stop and lobby everyone else to stop, or they do not, in which case the doom talk is marketing disguised as conscience.

I liked his analogy:

Imagine if the Ford Motor Company put out a report saying that it feared its popular F-150 trucks might soon start bursting into flames, but that there was nothing the company could do about it because automotive technology was too inevitable and important to slow down. You’re probably struggling to picture this scenario because no reasonable consumer product company would ever act like this.

No consumer-products company talks this way, and he thinks AI should not get a pass for it. (He also swings at the Fable and Mythos export-control mess, reading the administration’s move as calling Anthropic’s bluff. More on that below.)

I part ways a bit. Treating a frontier model like an F-150 assumes we already agree on what the product is for and how it fails, which is the thing still in dispute. I write more about this in the biosecurity tag here.

2. SecureBio puts its AI biology scores on one dashboard

SecureBio launched a public dashboard collecting its model evaluations on biosecurity-relevant tasks, from the Virology Capabilities Test to newer agentic benchmarks like ABC-Bench. It spans 9 companies and more than a dozen metrics across three years, rolled into a “Bio Capabilities Index” built on the same method as Epoch’s capabilities index. TLDR: frontier models now clear human-expert baselines on these tests and keep climbing.

3. RAND wants a cryptographic paper trail for AI-designed biology

A new RAND CAST report, Verifiable Audit Trails for AI-Enabled Biological Design Tools (Berke, Kilian, Griffin, Atanda, Vazquez) proposes a cross-tool, tamper-evident audit trail for AI-enabled biological design tools: hardware-bound cryptographic signing, hash chaining, and append-only transparency logs so a synthesis provider or regulator can verify how a sequence was produced. The building blocks are mature in adjacent fields, and the authors argue that the technical side is the easy part.

The report is admitting the real obstacles are not technical. Nobody is set up to govern shared infrastructure, the burden falls unevenly across the research community, and institutions lack the capacity to review audit records even if they had them.

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4. The anti-scaling law, and why AI might crowd drug development

Liang Chang’s essay was one of the the bests thing I read this week: The Anti-Scaling Law in Biology, and Why AI Could Make Crowding Worse Before Making Drug Development Better.

He starts from two real wins: Revolution Medicines’ pan-RAS inhibitor daraxonrasib, which moved median overall survival in treated metastatic pancreatic cancer from 6.7 to 13.2 months (HR 0.40), and Lilly’s VERVE-102 base editor, which dropped PCSK9 by up to 88% and LDL by up to 62% from a single infusion. Then he pushes back on the tech reflex that says the next hundred are queued up behind them.

His claim is an anti-scaling law. In software, proving one case lets you copy it a hundred times. In biology the first proof of concept usually consumes the single best target, because you never picked it at random, and the second is harder than the first.

AI raises the floor on execution (binder design, optimization, trial ops) without moving the ceiling on the step that decides whether a drug works, which is choosing the right target for the right disease. The odds a drug entering trials reaches approval sat near 10% in 2014 and about 8% by 2020, straight through the genomics and ML era. His fear is that cheaper engineering plus training-data gravity funnels everyone onto the same validated targets, so you get 300-plus CD19 programs instead of new biology.

5. Local models grew up

Vicki Boykis recently keynoted our Applied Machine Learning Conference here in Charlottesville. I loved her talk. You can watch it here.

In her most recent blog post, Running local models is good now, she makes the case that local models crossed a line in the last few months. On a 2022 M2 Mac with 64GB, she is now doing agentic coding locally with Gemma 4 at roughly 75% of frontier speed and accuracy, refactoring a notebook into modules and writing tests, all inside a Docker sandbox. Her test for whether a model is good enough is whether she still double-checks it against an API model, and she does that a lot less now.

The HN thread is a useful counterweight to the enthusiasm.

I don’t know about good, I use a lot of local models and they’re still pretty painful to run locally

You have dense models (qwen 27b, gemma 31b) who are pretty smart, but pretty slow

You have MoE models (gemma 26b, qwen 35b, north mini code 30b) who are pretty fast, but make a lot of mistakes

You need a lot of memory to run these well, quantization makes tool calling weaker, so most run at 4 bit quants and are wondering why it kinda sucks and that’s because you’ve essentially lobotomized the model (I recommend unsloth quants, i recommend 6bit for MoEs and 5bit for dense)

So you need a lot of compute to make the pre-fill fast, you need bandwidth to make the decode fast, you need a lot of memory to hold everything - lot of ifs

On top of that, your laptop becomes a loud hot churning machine, it’s uncomfortable to work with.

So are they good? not really. Do they work? yes

One commenter daily-drives Qwen3.6-27B and prefers it to the frontier models for his workflow precisely because he wants the model to type while he does the thinking, though another warns you should not generalize from Sonnet to a lab’s actual flagship. The recurring objections are cost (”some of us have a budget”) and reliability: small context windows, and local models slipping into hallucinated tool-call JSON, which is where agentic loops break. Vicki is describing a real shift for personalized, low-recency dev work, with the production bar still a step out.

6. minibwa retires BWA-MEM

Heng Li and Nils Homer released minibwa, and Li is direct about what it is: the full replacement for BWA-MEM.

Read the preprint. Instead of another drop-in clone that preserves identical output, minibwa makes breaking changes, splicing BWA-MEM’s variable-length seeding onto minimap2’s chaining and SIMD alignment, with ropebwt3’s SMEM search and aggressive memory prefetching. The result is about 4x BWA-MEM and over 2x BWA-MEM2 at comparable accuracy, under 20GB RAM, with native long-read and directional bisulfite support that BWA-MEM never had.

On accuracy, the comparison is closer than the speed numbers suggest. On simulated short reads BWA-MEM is slightly more accurate, and the difference comes entirely from centromeric and acrocentric regions where minibwa deliberately stops trying, since you cannot place those reads correctly anyway.

One footnote I enjoyed: the Rust rewrite, minibwa-rs, was mostly written by coding agents, which tells you something about where bioinformatics tooling is heading.

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In other news…

The Fable 5 and Mythos 5 shutdown: the Commerce Department issued an export-control directive citing national security, and Anthropic pulled both models for every customer to comply. Rounding up a few articles on the topic.

• Anthropic’s own statement frames it as a misunderstanding over a narrow, non-universal jailbreak (essentially asking the model to “fix this code”), and notes access to all other models is unaffected.

• The HN discussion of that statement runs the predictable split over whether this is a safety call or a political one.

• Katie Moussouris at Luta Security argues the export controls harm US cyber defense, since the flagged behavior is the same find-fix-test loop defenders run every day, and points to the freefable.org open letter.

• Transformer’s Shakeel Hashim reads it as an AI licensing regime arriving through the back door, arbitrary and post-hoc, built on export law never designed for this.

• Axios reports the shutdown came down to personality clashes and a communication breakdown with the administration, set off by Amazon’s Andy Jassy calling Treasury Secretary Bessent.

• The Atlantic argues the episode is how America loses the AI race.

• The Washington Post covers how Anthropic lost the White House’s trust, and then its flagship product.

• The NYT’s opinion page calls it the start of a new kind of conflict.

• The Superintelligence newsletter asks when a model becomes too powerful to stay public.

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And lastly, the ACM AI Letters Vol. 1 No. 2 (June 2026) is now available online.1 I want to read all of the articles in the new issue.

1. Balancing Comfort and Growth: A Dual-Mode Theory of Human–AI Development

2. Autonomy or Guidance: What Users Want from AI versus Human Advisors

3. AI Ethics Must Speak the Language of Business

4. Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships

5. Rethinking Scientific Practice in the Age of Artificial Intelligence

6. AI Collaboration and the Decentering of Human Creativity

7. The Harder Charge: Teaching Technical AI Literacy across Majors

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As with Dario’s previous (longer) essays this one’s a good read. Given what happened two days after this essay was published, I had to pause and read this section twice.

It is time to go beyond transparency to more serious and binding regulation of AI. I believe the best analogy, at least at the current stage of the exponential, is to cars, airplanes, or drugs—powerful technologies essential to the modern economy, but capable of killing large numbers of people if designed or operated poorly. I therefore believe we should model AI regulation on agencies like the Federal Aviation Administration (FAA). Frontier AI models, like airplanes, should be required to go through technical testing and auditing, and their release should be blocked or reversed as a threat to public safety if they do not meet high standards of safety. I am grateful to see the Trump administration’s Executive Order move incrementally towards a greater role for government in AI, though Anthropic’s proposal recommends even further action. Our proposal includes the following elements:

• Models above a threshold of compute should undergo mandatory testing by a qualified third party for their level of risk in four specific areas: cybersecurity, biological weapons, loss of control of AI systems, and automated R&D that could accelerate these other risks.

• The government should have the power to block or deter deployment of the model if it is determined, in light of third-party assessment, to present unacceptable risks. This power must be scoped to the above four specific risks and there must be protective measures against political favoritism or arbitrary decisions.

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Now I use Positron instead of RStudio, and Raycast (free version) instead of Alfred.1

With a few keystrokes (CMD-space to activate Raycast, type “pos” to search through projects in Positron) I can launch a project in Positron. Here’s what it looks like.

Here’s how to set that up. The gist is outlined in the Positron docs here.

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Positron + project manager

The first step is to install the project manager extension in Positron. Once you do that, the project manager appears as a stack of folders in the Activity Bar. When clicked, you gain access to a clickable, organized listing of your projects. Here’s mine, showing my favorite folders (with a redaction).

From here, in Positron, you can just click on a folder and it’ll open a new Positron window running in that project. This is essentially the same thing as double-clicking the .Rproj file in RStudio.

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Raycast

Next, install Raycast and the Visual Studio Project Manager Extension (yes, VS Code). In the settings, make a few changes, annotated below. Add “pos” as the alias, and in the options on the right, set “Positron” as your “VS Code”. I know that looks weird, but this is how it’s done.

Now, when you activate Raycast, you can type “pos” and you’ll bring up the Positron project manager.

From here you can select which project you want to open. Hit Enter and it’ll open Positron running in that folder/project.

The workflow is described here in the Positron docs.

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Here are my open browser tabs I have open that I hope to catch up on soon.

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Blogs/newsletters/etc

1. Dario Amodei — Policy on the AI Exponential

2. Paving the way for agents in biology \ Anthropic1

3. NIH RFI on limiting the number of grants per PI

4. Claude Fable 5 and Claude Mythos 5 \ Anthropic2

5. Eight Predictions for the Future of Higher Education

6. Matt Lubin: Five Things: June 7, 2026

7. Is AI More Expensive Than the Employees It's Replacing?

8. The Anti-Scaling Law in Biology, and Why AI Could Make Crowding Worse Before Making Drug Development Better

9. Sasha Gusev: Thoughts on AI in academia

10. How LLMs Actually Work | 0xkato

11. A cell is not a spreadsheet- why “Virtual Cells” are still mostly hype

12. Anthropic: When AI builds itself

13. Can A.I. Produce Writing That We Actually Want to Read?

14. Is a compute crunch coming?

15. Built to benefit everyone: our plan | OpenAI

16. Bots are scraping open data — how should researchers respond?

17. Why Are Cells Small? - Niko McCarty

18. How to build a cancer vaccine, and whether they will work this time

Papers

1. The total eclipse of bioinformatics: From disruption to convention, and a gentle warning

2. Dual-use artificial intelligence and biology: upstream risk-benefit reviews

3. Molecular de-extinction looks to the past to find the molecules of the future

4. AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation

5. Pleiotropic shared heritability quantifies the shared genetic variance of common diseases

6. Ten simple rules for teaching data science

7. Depth normalization for single-cell genomics count data and Lior’s explainer

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2

I haven’t had a chance to do anything with Fable yet, mostly because I work in AIxBio, and Bio is off limits. And because I’m a biologist, Fable refuses to talk to me (“Who am I?” leads to safety flags and demotion of the rest of the conversation to Opus). Precautionary principal is probably the right move here given the benchmarks, and I think managed access will likely be the way these models are released from here out.

I’m not an activist, and I don’t use this space for advocacy. As a rule, I keep my opinions about research politics and policy off the site (and anywhere else online in general). I’m breaking that rule today, because this reaches every part of the scientific research enterprise and whatever role the United States still hopes to play in it.

The Office of Management and Budget has proposed rewriting 2 CFR Part 200, the rule that governs how every federal grant gets spent. A Science editorial lays out what that would mean for research: every funding decision routed through political review, multiyear grants terminable with no due process, and case-by-case approval for any award that spends a dollar outside the US, which would effectively end most international collaboration. Steve Usdin of BioCentury, quoted in the editorial, called the drug industry’s silence “complicity in the destruction of US science.” The Science editorial ends with a rallying call:

, who spent >20 years as an NIH program officer, writes in an essay making the point that this is much larger than science. 2 CFR Part 200 is the universal framework for federal grants, so the same political override and discretionary termination provisions land on Medicaid, Title I schools, highway funds, and tribal health programs.

The comment period closes July 13. Ginexi’s how-to guide is a great place to start, and mechanics are important - since a thousand identical form letters count as one comment, so write your own, in plain language, and cite the specific provisions that would hurt your work. If you hold, have ever held, or in the future want to hold a federal grant, this is ten minutes well spent.

File a comment on OMB-2026-0034

Now, onto the five things that interested me this week. Most of this week circles one question, whether we can measure what AI models actually do and trust the measurement. OpenAI decides the answer is yes and is shipping a life-sciences model to vetted partners.

1. Open models stay (only!) about four months behind the frontier.

2. Where evals actually sit in the biorisk evidence stack.

3. DARPA and NSF want universities for AI Forge (bring an IP-sharing agreement).

4. Two papers demonstrate AIxBio refusals tell you almost nothing.

5. OpenAI hands GPT-Rosalind to biodefense partners.

1. Four months off the pace

Epoch AI put a number on this open/frontier model gap: since January, the best open-weight models have trailed the closed frontier by about 4 months, or 8 points on Epoch’s Capabilities Index, roughly the distance from GPT-5 to GPT-5.5.

The “four months” figure uses a generous definition of catching up: an open model counts if it beats the old frontier in at least 5% of bootstrap samples. Epoch also notes that closed labs sit on their most capable models for safety or commercial reasons, so four months probably understates it.

As frontier models move to token/usage-based pricing, the open option is real and usable, they’re not terribly behind on some benchmarks. Whether those benchmark points translate into the work you actually do is a separate question, and section 4 suggests the answer is messier than a single index implies.

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2. Best evidence per buck: AIxBio evals

at answers a recurring jab at AI biorisk benchmarks (they don’t measure what we care about, so why bother) by placing evals in a four-tier hierarchy of evidence.

SecureBio

The Role of Evals in the Biorisk Evidence Hierarchy

We frequently hear questions along the lines of “what is the theory of change for benchmarks?“ or “evals aren’t measuring what we actually care about, so why the effort?“ Tl;dr: evals provide better evidence than just arguing from first principles, they are much cheaper and repeatable than wet lab uplift studies, and can help us to rule out capabilities…

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17 days ago · 10 likes · 2 comments · SecureBio and Jasper Götting

Tier 1 is arguing from first principles, which got the field started but convinces no skeptic. Tier 2 is evals, where we are now. Tier 3 is real-world uplift studies, the gold standard and the expensive one. Tier 4 is an actual incident, which nobody wants as a data point.

evals provide the best cheap, comparable and repeatable evidence we can get

I really liked the section Götting calls the rocky terrain between tiers 2 and 3, so much that I’m quoting the whole segment in full below. A well-powered uplift study costs millions and goes stale the moment the next model ships. Jasper proposes a bridge: if you collect rich data on how participants used the model during an uplift study, you can map their failures and successes back onto benchmark tasks, building what he calls correlates of uplift. That gives you a cheap proxy you can re-run on every new model instead of re-running the whole study. A small number of expensive studies can underpin a much larger cheap monitoring effort.

Assume you ran your large uplift study, found some effect, and two months later, the new generation of frontier models is released. Can you make any sensible claim about the impact on real-world risk of these models (besides “probably as good as the last gen“) without re-running everything? Maybe! You carefully approach the edge of the cost cliff you valiantly scaled and peer down to the vast plain of evals again.

If rich, qualitative data on LLM usage were collected during the uplift study, you should in principle be able to map the failures and AI-assisted successes of your study participants onto benchmarks measuring a corresponding capability. If model A failed to guide participants around a common methodological pitfall, and model B didn’t, is the same pattern detectable in a benchmark task about this method? Is the delta in overall uplift-effectiveness between models or model generations the same as the delta in the relevant benchmark results? Did you find interesting model differences and behaviors that don’t yet map onto a benchmark, but such a benchmark could be made? Did you encounter missing refusals to questions that should be refused in hindsight? All these can let you draw connections between evals and real-world uplift studies.

Finding these connections and correlations between uplift studies and in silico evals (“correlates of uplift”) is extremely important as it provides you with an OOM cheaper estimator for the underlying model capability that you can tweak and run over and over again for every newly released model; and, crucially, also for pre-release testing and risk assessment.

Jasper is blunt about the failure modes (specification gaming, underelicitation, sandbagging, weak construct validity), but the point stands: in biorisk, a false negative costs vastly more than a false positive, so waiting for tier 3 certainty has a price that critics rarely put on the books.

See also:

1. Hong, S. Z. et al. Measuring Mid-2025 LLM-Assistance on Novice Performance in Biology. Preprint at https://doi.org/10.48550/arXiv.2602.16703 (2026).

2. Zhang, C. B. C. et al. LLM Novice Uplift on Dual-Use, In Silico Biology Tasks. Preprint at https://doi.org/10.48550/arXiv.2602.23329 (2026).

3. Paskov, P. et al. RCTs & Human Uplift Studies: Methodological Challenges and Practical Solutions for Frontier AI Evaluation. Preprint at https://doi.org/10.48550/arXiv.2603.11001 (2026).

3. Forging ahead with AI Forge

DARPA and the NSF, with CAISI at NIST, announced AI Forge, a program to fund university research on 3 problems: AI interpretability, AI control, and adversarial robustness. The structure is a forum of universities, frontier AI companies, and government, running what DARPA calls Project Ventures, fast one-year university efforts from $750K to $3M or more, several per year. The Critical AI Challenges report alignts in part with this week’s other items, heavy on whether you can evaluate, interpret, and trust model behavior. One of its challenges is evaluating AI systems for scientific discovery once they outrun human expertise.

The RFI (DARPA-SN-26-80) asking universities to describe their capabilities is due June 22 at 5 PM ET. Two requirements are worth noting before spending too much time on a response (which must come from your VPR/Provost, not your individual lab). Key personnel are limited to US citizens and permanent residents, and the IP generated is meant to be shared across the forum, preferably under the MIT License.

This RFI is targeting U.S. universities. Further, AI Forge Project Venture designated key personnel (e.g., Principal Investigators [PIs], Co-PIs, and key research personnel) are anticipated to be limited to U.S. citizens or permanent residents.

Please note that Intellectual Property (IP) generated from Project Ventures is intended to be shared among AI Forge forum participants, preferably under liberal open-source licensing (e.g., the MIT License). Universities unable or unwilling to agree to a shared IP framework should not respond to this RFI.

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4. Refusal theater

Two preprints landed this week on we evaluate model safety in biology: we count how often a model refuses, and almost never check whether the refusal means anything. Both are preprints, and one is a hackathon project rather than peer-reviewed work, so weight them accordingly.

BioRefusalAudit, by Caleb DeLeeuw, built over a single weekend on a consumer GPU, asks whether a refusal is structurally sound or melts away under small changes. The results across five small models are not reassuring. Gemma 2 2B never refused across 75 prompts; it just hedged. Gemma 4 refused 65 of 75 prompts with chat-template formatting and 0 of 75 without it, and both Gemma models dropped to zero refusals once you capped output at 80 tokens, the kind of cap production systems use for cost and latency. Qwen and Phi-3 swung the other way, flagging 83 to 87% of benign biology as hazardous. The psilocybin probe was interesting: some models refused to discuss cultivating a legal-to-possess, biologically non-toxic substance more often than genuinely hazardous biology, which suggests refusal is tracking legal status and cultural taboo rather than actual CBRN risk.

RefusalBench, from Applied Scientific Intelligence, is the heavier study: 141 prompts in matched triples that hold the task constant and vary only biological risk tier, run across 19 frontier models. On identical prompts, strict refusal rates ran from 0.1% to 94.6%. Jurisdiction didn’t predict refusal, but provider did, with Anthropic’s API stack predicting refusal at an odds ratio around 21. The authors are careful that this is an access-path effect, not a statement about model weights: 99.8% of Anthropic’s refusals carried the same canned policy reason code, consistent with template-based filtering rather than case-by-case reasoning.

The TLDR I took away from this is that refusal rate misranks safety.

Grok 4.20 (lol, seriously?) had the best tier discrimination in the panel while ranking only seventh by refusal rate, and Claude Opus 4.7’s discrimination dropped 65% from the prior version, driven entirely by refusing more legitimate research prompts (benign-tier false positives jumped from 33% to 77%) with no gain in catching dual-use ones. 9 of 18 frontier models showed a hedge-but-help pattern at the dual-use tier.

Put together, a 4B model on a laptop and the frontier lineup all tell the same story in that refusal rate is close to noise as a safety signal. Thinking of wiring an LLM into a protein design pipeline? A more conservative model isn’t a safer one if it terminates your legitimate workflow at step 2, and the RefusalBench authors found the most-refusing models were often the worst at telling benign from dangerous. That a vendor selling agentic biology tooling ran this study is worth noting, but the matched-triple design and the should-refuse control set make it hard to write off.

All the data and prompts are available at github.com/AppliedScientific/refusalbench.

5. Rosalind plays defense

OpenAI launched Rosalind Biodefense, sponsoring access to GPT-Rosalind, its life-sciences reasoning model, for vetted developers building biodefense tools, and extending trusted access to select US government and allied partners. The launch cohort includes Fourth Eon and SecureBio on the screening and detection side, with LLNL, Johns Hopkins APL, and CEPI on the government side. The pitch is “defensive acceleration,” putting frontier capability in defenders’ hands first.

A vetted-access model is a sensible way to deploy a tool whose offensive and defensive uses are the same capability pointed in opposite directions, which is exactly why OpenAI gates it similar to what Anthropic did with Mythos. It is also a way to expand deployment of a model the company itself treats as High Capability in biology (the threshold it first crossed with ChatGPT agent last July) while keeping the optics clean (take “meaningfully advantage defenders” as a goal not a demonstrated result, yet!).

This dropped the same week as the OpenAI Foundation’s resilience announcement (more below) and a new cyber-focused executive order, so the bio, cyber, and safety messaging is moving together. And SecureBio appears here as a detection partner, in section 2 as the source of the evals argument, and again in the community roundup below. For a field this consequential, it runs on a small but mighty number of organizations doing important and exciting work!

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In other news…

(Section drafted/organized with Claude Opus 4.8)

• Frontiers’ open special issue, Artificial Intelligence and the Future of Biosecurity (edited by Bin Hu of Los Alamos, Dov Greenbaum of Yale, and Michelle Holko at Berkeley), collects perspectives and frameworks on the dual-use problem of AI-enabled biology. Worth a scan rather than a read-through:

  • Glycol vapor pathogen disinfection: a defensive technology for emerging biothreats

  • Automated Laboratory Security Tiers

  • Aligning innovation and security in AI-enabled biotechnology

  • Toward relational biosecurity

  • Know your scientist: KYC as biosecurity infrastructure

  • Protein design, generative AI and biological security

  • Methods for safely sharing dual-use genetic data

  • Without safeguards, AI-Biology integration risks accelerating future pandemics

• The June GCBR organization update rounds up Active Site, the Asia Centre for Health Security, IBBIS, and SecureBio.

• at always has a great weekly recap, so much so that I model the recap here off of his weekly “Five Things” series. His Five Things: May 31, 2026 post covers Illinois SB 315, ESMFold2, papal encyclical, Opus 4.8, OpenAI’s Rosalind Biodefense.

• A new executive order on AI innovation and security sets up a “covered frontier model” designation, a Treasury-run vulnerability clearinghouse, and a voluntary window of up to 30 days of early government access to qualifying models before wider release.

• The OpenAI Foundation detailed an AI Resilience program across four areas (bio, cyber, model safety, and effects on young people), saying it is finalizing more than $130M in grants and intends to commit over $1B in the next year.

• openRxiv (Richard Sever and Tracy Teal) sketched where bioRxiv and medRxiv are headed on trust signals as AI makes fake-but-plausible research cheaper: verified data deposits, funder metadata (now on roughly 80% of papers), and the hard problem of identity verification, since ORCID disambiguates authors but doesn’t confirm they are who they claim.

• NeurIPS desk-rejected 178 position-paper submissions (18.4%) for being substantially AI-written, using the Pangram detector, with another 123 asked to prove human authorship.

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I’ve developed a similar habitual relationship with AI. I reach for it thoughtlessly when I see an essay that’s a little longer than I want to read,2 or when I don’t feel like writing that cover letter for a manuscript,3 or when I want to avoid the mental strain of thinking hard about the architecture of whatever code I’m working on.4

AI deskilling is real. A couple weeks ago I was writing specific aims for a proposal with a few brilliant colleagues here in SDS, and I felt the constant urge to open up a Claude window. Just to write aims! In another setting, while in a coaching session with one of my CSTE Data Science Team Training teams last week, I opened Positron to demonstrate some fairly basic tidyr rectangling I’ve done hundreds of times, and couldn’t easily remember how to use the pivot_*() functions. It felt like coming back to a foreign language after a few years without using it.5 AI deskilling is real, and if you use these tools with any regularity you know it deep down.

Next month I’m doing an AI Dry July.6 I invite you to join me.

I’m going to resist using generative AI as much as possible for the whole month. With alcohol in Sober October the rule is easy —7 if it contains ethanol, I’m not having it. It’s a little more difficult with AI. You can’t do a web search or read a product without AI overviews. But generally, I’m not going to reach for ChatGPT or Claude for reading, writing, or for most coding problems.8 I’m teaching a new class this fall (Genomics Foundations for Data Scientists). I’m going to ask my students to struggle through some of the readings and assignments,9 so it’s only fair if I struggle through creating those assignments and course materials. I’m exhausted from reading AI slop in everything from academic literature to the New York Times to many of the blogs and newsletters I personally recommend, so it’s about time to stop compulsively reaching for AI to polish my own writing.10

I don’t really care that my mental arithmetic is terrible because of calculators, or that my navigation skills aren’t what they were back in the pre-GPS days of the 90s (the best decade). I want to avoid the habitual slide into offloading some part of nearly every cognitive task to the chatbot. At least for a month, to re-center myself and take stock of where the deskilling is creeping in.

I know I’ve written very positively about AI in this newsletter, in everything from writing code to peer review. And I’ll continue to do so. I’m neither an AI evangelical nor an AI doomer, and I’ll highlight problems where I see them. Even in 2026 it’s possible (gasp!) to have nuanced views. And I’m starting to think habitual and compulsive AI use is probably not that great for me over the long run.

We’ll see how this works out. I’ll write something at the end of the next month on how it went and what I took away from it.

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I’ve taken a couple of these. They’re short. If it matters to you, when you complete the course you can get a certificate that you can wire up to your LinkedIn profile, which link out to the verified cert (example).

Full list as of this week below.

Claude 101: Learn how to use Claude for everyday work tasks, understand core features, and explore resources for more advanced learning on other topics.

Claude Code 101: Learn how to use Claude Code effectively in your daily development workflow.

Introduction to Claude Cowork: Learn to work alongside Claude on your real files and projects. This hands-on course covers the Cowork task loop, plugins and skills, file and research workflows, and how to steer multi-step work responsibly, so you’re productive in your first week.

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Claude with Amazon Bedrock: As part of an accreditation program created for AWS, Anthropic launched a first-of-its-kind training for AWS employees. Here’s the full course so you can follow along.

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Teaching AI Fluency: This course empowers academic faculty, instructional designers, and others to teach and assess AI Fluency in instructor-led settings.

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AI Capabilities and Limitations: An introductory course about how AI works.

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1. Choosing to stay human, and what 370,000 college essays say about that

Two pieces from different authors converging on a similar point, and one I’ll be writing about next week as well.

Ethan Mollick’s essay Choosing to Stay Human isn’t necessarily arguing against using AI or even AI for writing, but focuses on what to hand over. He’s fine offloading phone numbers, arithmetic, cursive. He’s not fine reflexively offloading writing, because writing is one of those tasks where the doing is the point. He cites his own BCG consultant study and a pair of Wharton-affiliated education experiments where small differences in classroom AI deployment produced opposite outcomes. In Turkey, students with plain ChatGPT did worse on tests than students without it. In Taipei, students with an AI tutor that gave personalized problem sequences scored 0.15 SD higher, roughly six to nine months of additional schooling.

Related is companion piece is Rebecca Winthrop’s NYT op-ed on a Georgetown research program led by Adam Green that has been tracking college application essays before and after ChatGPT. In a study of 370,000+ personal statements, post-ChatGPT essays got rated as more creative by human judges while offering fewer novel ideas. A separate study found human-written essays contained up to 8x more new ideas than AI-assisted ones. The homogenizing effect was largest on students furthest from the mean, including neurodivergent students and racial and linguistic minorities. I.e., the kids whose voices are most distinctive are the ones most flattened by the assist.

Stay tuned next week. I’m writing a very short piece on the topic.

2. The despair of the professor

The natural sequel is Jay Caspian Kang’s New Yorker piece, “The Despair of the Professor in the Age of A.I.” collecting testimonials from faculty members about what AI has done to their teaching. Hard to read. But nothing in it is surprising (I’d guess most academics have heard versions of these stories from colleagues).

The piece opens with Jane Sloan Peters, a religious studies professor at Mount Saint Vincent, describing a course she’d taught for years called “Letters from Prison.” Before AI, students struggled to land on themes and arrived somewhere personal through that struggle. Last year, every one of her students turned in something polished and empty.

From Beth Ritter-Conn at Belmont, on her honors students:

The tipping point was last year when I had Honors students—Honors students!—using A.I. to write reflection journals. Literally the only task there is “tell me what you are thinking inside your own head.” There is no right or wrong answer. It’s just, Give me your thoughts on this thing. And I had students who outsourced that task to the robots.

From Susanna Boxall, a philosophy lecturer at Chico State, on online classes:

Now, online classes are a simulacrum of education: the students pretend to learn, and I have to pretend that I am teaching them something.

From Neal Hebert at Grambling State, who teaches theater and now assigns plays too obscure for ChatGPT to know about:

I’ve stopped being a collaborator in these intro courses and started being a plagiarism cop, and I do resent that a bit.

And from Jeremiah Croster, who teaches English at Houston City College:

The get-a-degree approach was already winning even before A.I., but now that it’s here, the education part is starting to feel like something someone will write about in a history book. Or maybe A.I. will do it.

Not every voice in the piece is despairing. Auyon Siddiq at UCLA Anderson made his stats exam fully AI-permitted and reports the average is still 75%, because students who don’t understand the material aren’t saved by the tool. Daniel Silver at Toronto Scarborough redesigned his sociology assignments as multi-agent simulations and says the best final projects showed more creativity than what he used to get. So it’s not uniformly grim, but the dominant note is loss.

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3. ESMFold2 and the protein biology “world model”

Biohub (formerly known as EvolutionaryScale, now merged with the CZ Biohub network) announced ESMFold2 and a new ESM Cambrian (ESMC) family of protein language models this week, alongside an atlas of 6.8 billion protein sequences with 1.1 billion predicted structures. The paper is long and I haven’t read it, so I’ll be careful here.

What Alex Rives says in his thread, in his words: ESMFold2 is state of the art on protein interactions, especially antibody-antigen complexes. They designed and experimentally validated miniprotein binders and single-chain antibodies against five therapeutic targets with good hit rates including a PD-L1 minibinder that restores T-cell signaling at functional IC50 comparable to atezolizumab. They describe this as evidence that the model has materialized “a world model of protein biology.”

What is a “world model” anyway? Seems like “world model” is doing a lot of marketing work in AI right now, with no agreed-on technical meaning. Whether calling it a “world model” adds anything beyond “the representations are structured in biologically meaningful ways” is a fair question, right?

Either way, a quick look at their press release benchmarks and binder design results all look exciting. I’m looking forward to seeing what holds up under peer review.

4. Pope Leo on AI

Jill Lepore’s New Yorker piece on Pope Leo XIV’s new encyclical Magnifica Humanitas provides a nice overview for those of us without time to read the 40k+ word papal document on AI. Tech leaders have spent 20 years borrowing the rhetoric of mission, salvation, and disruption from religion, and the first American Pope, who took his name in reference to Leo XIII’s 1891 Rerum Novarum on industrial labor, has decided to reclaim the territory. From Lepore:

Little in the encyclical is surprising; its force lies in its being said all at once.

I haven’t read the whole encyclical yet so I’m not going to summarize it. It’s interesting that (and this is partly Lepore’s read but also visible in a quick skim of the text itself) part of the encyclical is about language and writing. There’s a whole subsection on “an ecology of communication.” The Pope and Ethan Mollick and Rebecca Winthrop in #1 above are, in different vocabularies, worried about the same thing, that we are losing the conditions under which humans develop their own thoughts.

Our first task is neither to demonize nor idolize technological tools, but to utilize them on the basis of a fundamental principle, namely that truth is a common good and not the property of those with power or influence. We must therefore promote an ecology of communication.

Just pausing to say here that I find it amusing and puzzling that the company I keep who’d normally be first in line to criticize the Church, its anti-scientific doctrine, and its history of abuses, are now lining up behind the Pope as soon as he says something critical of AI. Cat’s post on the topic stuck with me.

5. NIH’s reinterpretation of “foreign component”

Jeffrey Brainard at Science reports that NIH grant managers are privately telling investigators they need pre-approval for any co-authorship with a scholar affiliated with a foreign institution, even when all the work was done in the United States. NASA is reportedly doing something similar for collaborations with researchers in China. Neither agency has issued public guidance.

The mechanism is that NIH has long required approval for grants with a “foreign component,” historically defined as significant scientific work performed outside the U.S. Now NIH is suggesting that mere coauthorship with someone at a foreign institution now counts as a foreign component, regardless of where the work happened. NIH is asking grantees to remove papers with foreign coauthors from annual progress reports if no foreign component was originally approved. An NIGMS email seen by Science asks institutions to promise that flagged U.S. authors won’t collaborate with the relevant foreign coauthors in the future.

This creates pressure to remove foreign-affiliated coauthors from papers before submission, which violates standard publication ethics. Foreign-affiliated coauthors include visiting students and postdocs temporarily in the U.S., collaborators who donated reagents but didn’t run experiments, and U.S.-trained scientists who moved abroad after the work was done. The rule is seems to be administered through private emails rather than formal guidance which means it can shift without anyone being able to point to the rule that changed.

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AI Exchange @ UVA Substack

OpenScholar Synthesizes Scientific Literature and Reduces Hallucitations

This is a guest post from Stephen Turner. Dr. Turner is an Associate Professor of Data Science and Assistant Dean for Research in the University of Virginia School of Data Science. He writes regularly about AI, data science, biomedical research, and biosecurity in his newsletter…

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a month ago · 7 likes · 4 comments · Stephen D. Turner

A recent Nature paper from UW and Allen AI introduces OpenScholar, a retrieval-augmented language model that outperforms GPT-4o1 on scientific literature tasks despite being a fraction of its size.

Asai, et al. (2026). Synthesizing scientific literature with retrieval-augmented language models. Nature. https://doi.org/10.1038/s41586-025-10072-4

OpenScholar addresses a problem with using LLMs for research: citation hallucination.2 When asked to cite recent literature across fields like computer science and biomedicine, GPT-4o3 fabricates citations 78-90% of the time. The references look plausible, but the papers simply don’t exist.

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What the paper showed

OpenScholar tries to ground responses in a data store of 45 million open-access papers with 236 million passage embeddings. Instead of generating citations from parametric memory, it retrieves relevant passages, synthesizes them, and iteratively refines its output through a self-feedback loop. The model drafts a response, critiques it, retrieves additional context if gaps are identified, and revises. This cycle continues until the output meets quality thresholds for factuality and citation accuracy.

On ScholarQABench (a new benchmark the team developed with nearly 3,000 expert-written queries), OpenScholar-8B outperformed GPT-4o by 6.1% in correctness and beat PaperQA2 by 5.5%.

When the team ran human evaluations with 16 PhD-level experts across computer science, physics, and biomedicine, experts preferred OpenScholar’s responses over human-written answers 51% of the time (70% when using GPT-4o as the backbone).

Open source

Everything is released: models, training data, data store, retrieval indices, benchmark, and a public demo. The demo has already attracted 30,000+ users generating 90,000 queries.

• Paper: https://www.nature.com/articles/s41586-025-10072-4

• Training data: https://huggingface.co/datasets/OpenSciLM/OS_Train_Data

• Benchmarking: https://github.com/AkariAsai/ScholarQABench/tree/main/data

• Queries: https://huggingface.co/datasets/allenai/openscilm_queries

• OpenScholar code: https://github.com/AkariAsai/OpenScholar

• Code to run the evals: https://github.com/AkariAsai/ScholarQABench

• Public demo: https://openscholar.allen.ai

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Limitations

A notable limitation: the benchmarking predates current frontier models, and only compares OpenScholar to now-deprecated ancient models. recently remarked on a different paper to this effect:

That is: Benchmarks age fast. If a weaker model is close, a better model will often clear the bar later, and we learn little from the gap.

GPT-5.5 with extended thinking, Claude Opus 4.7, and commercial tools with deep research capabilities (like Gemini Deep Research or Perplexity Pro’s updated pipelines) remain untested. Whether domain-specialized RAG maintains its edge against these newer systems is an open question.

See also

See also my previous post on Consensus, and wiring up your own Zotero library to it, to be able to ask and answer questions about your own literature collection, find gaps, and fill those gaps in.

Come to my workshop

On June 17 2026 at noon I’ll be teaching a workshop for UVA’s AI Research Initiative AI upskilling series: Smarter Literature Reviews with AI-Powered Tools. You can register for the workshop here. Grab your spot now!

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1. ’s case for shaping AI-for-biology before it shapes us

2. RAND and Helena on AIxBio mitigations

3. on the state of AI in genomics

4. Nature’s AI scientists week, and the editorial pushback

5. 45 expert scientists review the reviewers

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1. Pannu’s three-part case for shaping AI-for-biology

(Johns Hopkins, Center for Health Security) published a three-part series last week on shaping AI progress for biology and biosecurity.

Part 1 sets up the series. AI will compress decades of biological research into years, but cures won’t arrive by default, and the same systems that enable them can lower the barrier to weaponizing pathogens. We need proactive policy on both sides.

Jassi Pannu

Part 1: Shaping AI progress for biology and biosecurity

This essay is Part 1 in a series on why we should leverage AI to advance biomedicine, while simultaneously building our resilience to biological risks. If you have ideas for how to shape AI progress for biology and biosecurity, submit to IFP’s The Launch Sequence…

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a month ago · 9 likes · Jassi Pannu

Part 2 is where it gets interesting. Pannu lays out what she calls autonomous biological discovery: AI systems that automate every step of the research cycle, including managing the cycle itself, with orgs like Isomorphic Labs, FutureHouse, Ginkgo Bioworks and others entering the fray.

Evolution selects for reproductive success and gets stuck in fitness valleys. AI-driven design doesn’t have that constraint. Whether that’s a feature or a terrifying bug depends on what you’re designing.

Jassi Pannu

Part 2: Where is AI for biology headed?

This essay is Part 2 in a series on why we should leverage AI to advance biomedicine, while simultaneously building our resilience to biological risks. If you have ideas for how to shape AI progress for biology and biosecurity, submit to IFP’s The Launch Sequence…

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a month ago · 6 likes · 2 comments · Jassi Pannu

Part 3 centers on smallpox eradication: 171 years between Jenner’s cowpox demonstration in 1796 and Henderson’s 1967 campaign, and only 10 of those years were spent actually eradicating. Her conclusion is that the bottleneck wasn’t tech, it was coordination and political will, so even if AI drives the marginal cost of biology research to zero, we shouldn’t expect cures to deploy themselves.

Jassi Pannu

Part 3: Where AI will fall short for solving disease, and what to do about it

This essay is Part 3 in a series on why we should leverage AI to advance biomedicine, while simultaneously building our resilience to biological risks. If you have ideas for how to shape AI progress for biology and biosecurity, submit to IFP’s The Launch Sequence…

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a month ago · 6 likes · 3 comments · Jassi Pannu

2. RAND and Helena on AIxBio mitigations

RAND and Helena released the conference proceedings (full PDF here) from a January 2026 workshop on AI-enabled biological threats. 22 participants from frontier labs, biotech, biosecurity, and academia, working over two days in DC under Chatham House rules, with three threat scenarios: a millenarian nonstate group releasing a novel influenza A, an agroterrorism scenario targeting US wheat with an engineered fungal pathogen, and a state-sponsored insider attack on a semiconductor plant using a biofilm-forming bacterium. Scenarios were deliberately compressed and the document withholds specifics for infohazard reasons.

The pandemic group prioritized pathogen-agnostic physical defenses (high-quality PPE, indoor air quality with filtration and UV), a voluntary credentialing system called “BioTrust” modeled on ORCID, and AI “guardian models” for intent monitoring. The agroterrorism group went after holistic biosurveillance, information-sharing modeled on the Kansas Intelligence Fusion Center, and synthetic DNA screening for agricultural pathogens (which gets less attention than human-pathogen screening, and the participants thought that was the most well-scoped problem of the bunch). The critical infrastructure group went hardest on LLM-side interventions: investing in safeguards to better infer intent from prompt patterns, information-sharing between LLM companies via something like the Frontier Model Forum, federated cross-platform behavior analysis, and (this one is interesting) training LLMs to de-escalate malicious intent by adapting techniques from suicide prevention hotlines.

The participants (which included Twist Bioscience, Anthropic, Microsoft, SecureBio, Los Alamos, and others) kept running into the same wall, which the report names explicitly:

A central theme was that technical feasibility and political backing together determine a mitigation’s success.

I.e., most of these will fail without sustained funding and political will, and almost none of them have either. The other recurring caveat was attribution. AI-enabled biological incidents may remain unattributed indefinitely, which weakens deterrence and complicates response authority.

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3. Seven points on AI in genomics

ran the University of Chicago’s annual genetics, genomics, and systems biology symposium last week, and turned the speaker lineup into seven points on the current state of AI in genomics.

Ran’s Substack

Seven points on the current state of AI in genomics

Last Friday, the Committee on Genetics, Genomics & Systems Biology hosted its annual symposium at the University of Chicago, this year on the theme of AI in Genomics. We brought together six speakers whose work spans much of the interesting territory in the field right now…

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a month ago · 23 likes · 2 comments · Ran Blekhman

The whole post is worth reading (<10 minutes). I’m just going to highlight a few.

First, the scaling laws may not hold for DNA. Evo 2 is 40 billion parameters trained on 9 trillion nucleotides spanning every domain of life. But Vishniakov et al. (2025) compared seven genomic foundation models against randomly initialized baselines of matched architecture across 52 tasks. The random baselines often matched or beat the pretrained models. Tang et al. (2025) found that raw one-hot encoded sequences are competitive with learned DNA-LM representations on regulatory genomics tasks. As Alex Lu put it at the symposium, DNA isn’t natural language: low signal-to-noise, vast repetitive tracts, no obvious word or sentence analogs, and sparse functional elements that interact combinatorially across long distances.

Second, Arjun Krishnan’s rule of thumb on benchmarks:

The best model is usually the one that is consistently number 2 in benchmarks across the literature.

Whoever publishes a model also designs the benchmark, and the benchmark almost always flatters the model. A model that’s consistently competitive but rarely first-place is more likely to be genuinely strong than one that wins on the benchmark its own authors built. I’m stealing this.

Third, toward the end, is a succinct rule for trainees using AI tools:

This is the cleanest articulation of the trainee-and-AI problem I’ve seen. I’ve written about this before, highlighting work from a new colleague and co-author, Arjun Krishnan:

The friction of doing it the slow way is often the friction of actually learning, the “productive struggle” I’ve written about here before. An AI tool that produces output you can’t evaluate is just a black box you’re forced to trust. I’d extend that beyond trainees, frankly.

4. Nature’s AI scientists week, and the editorial pushback

On Tuesday, Nature published three full-length papers on AI scientists, an editorial that hedges, and a comment piece that pushes back. All on the same day.

1. Ghareeb, A. E. et al. A multi-agent system for automating scientific discovery. Nature 1–3 (2026) doi:10.1038/s41586-026-10652-y.

2. Aygün, E. et al. An AI system to help scientists write expert-level empirical software. Nature 1–3 (2026) doi:10.1038/s41586-026-10658-6.

3. Gottweis, J. et al. Accelerating scientific discovery with Co-Scientist. Nature 1–3 (2026) doi:10.1038/s41586-026-10644-y.

4. Messeri, L. & Crockett, M. J. The uncritical adoption of AI in science is alarming — we urgently need guard rails. Nature 653, 675–676 (2026).

5. Why AI cannot do good science without humans. Nature 653, 650–650 (2026).

The three papers: Google DeepMind’s ERA, an LLM-plus-tree-search system that discovered 40 novel single-cell analysis methods that outperformed the top human methods on a public leaderboard, and 14 COVID hospitalization forecasting models that beat the CDC ensemble. Google’s Co-Scientist, a multi-agent system built on Gemini that helped identify in vitro–validated drug repurposing candidates for acute myeloid leukemia and (in a now-famous demo) recovered an antibiotic-resistance hypothesis that a Imperial College team had spent a decade developing but hadn’t yet published, in days. And FutureHouse’s Robin, which autonomously proposed enhancing RPE phagocytosis as a strategy for dry AMD, identified ripasudil (a clinically used ROCK inhibitor never previously proposed for AMD) as a candidate, validated it in vitro, then proposed an RNA-seq follow-up that fingered ABCA1 as a possible novel target. All hypotheses, experimental directions, data analyses, and main-text figures in the Robin paper were produced by Robin.

These are real results. With that throat-clearing out of the way—

Then there’s the editorial, “Why AI cannot do good science without humans”, which is mostly anodyne until the closing paragraph:

Again: not every grant proposal need include an AI project.

Once more: not every grant proposal need include an AI project.

I read this as the editorial board, deliberately, on the day they published three papers about AI scientists, telling reviewers and program officers not to use “no AI angle” as a reason to triage a proposal.

The comment piece is more pointed. Lisa Messeri (Yale anthropology) and M. J. Crockett (Princeton psychology) wrote “Uncritical use of AI in science needs reality check”. Some empirical claims: Hao et al. (2026) analyzed 41.3 million papers across biology, medicine, chemistry, physics, materials science, and geology and concluded that AI adoption seems to “induce authors to converge on the same solutions to known problems rather than create new ones.” I wrote about this earlier this year:

Kusumegi et al. (2025) looked at 264,125 papers and found that in LLM-assisted papers, good writing stopped being a useful heuristic for scientific quality. Organization Science audited 6,957 submissions from 2021 to 2026 and found LLM-assisted papers had poorer scientific quality by acceptance rate. The closing argument is about deskilling: cleaning raw data, reading and summarizing the literature, the entry-level grunt work that AI is now offered as a solution for, is also how scientists develop the tacit knowledge needed to supervise AI-assisted workflows. If trainees don’t develop those skills, who oversees the AI?

Three papers showcasing autonomous discovery, an editorial gently telling reviewers not to fetishize AI angles in proposals, and a comment piece arguing that the productivity gains may be hollowing out the next generation. Read them together. Or, if you don’t have time, listen to Nature’s podcast.

5. 45 expert scientists review the reviewers

One more, and this connects to a paper I co-authored. A preprint went up at arXiv:2605.20668 titled “On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists.” Big study. 45 domain scientists annotated reviews of Nature-family papers, comparing official human reviewers against three frontier LLM agents. Headline results:

• On aggregate review-item quality, all three AI reviewers exceed the lowest-rated human, and GPT-5.2 exceeds the top-rated human.

• AI reviewers raise more significant items but with lower correctness.

• Replacing one human reviewer with one AI reviewer minimally erodes panel diversity, because human reviewers themselves surface largely disjoint sets of criticisms.

• AI reviewers can augment but not replace a human panel.

• Current frontier AI reviewers in an agentic framework provide genuine value on the rigor- and code-heavy aspects of peer review, while systematically failing on the field-context aspects.

On that last point: it’s the same argument that Agnieszka Swiatecka-Urban, Arjun Krishnan, and I argued for in our preprint earlier this year.

Our claim was that AI is best deployed as a rubric enforcer for the systematic, criterion-checkable parts of review (consistency between scores and comments, statistical reporting, completeness of evaluation, internal consistency of reviewer reasoning) while humans retain authority on the parts that depend on argumentative-world knowledge (novelty, feasibility, recognizing creative leaps, judging whether an ambitious proposal might fail spectacularly or succeed brilliantly). The arXiv paper, working with a completely different methodology and 45 domain scientists doing item-level annotation of real Nature-family reviews, lands in the same place. AI is strong on rigor and code, weak on field context.

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My friend and colleague VP Nagraj published a new R package called ctrlvee that makes this a lot easier.

• CRAN: https://cran.r-project.org/package=ctrlvee

• GitHub: https://github.com/vpnagraj/ctrlvee

It does one thing. Put your cursor anywhere in an R script in Positron or RStudio, call the add-in, provide a URL, and a few milliseconds later you’ll have all the code from that page in your editor, separated by chunk boundaries (along with some metadata and a note to check the license!).

The package README provides a demonstration using the “Data Validation and QA” chapter of my Data Science Team Training book (dstt.stephenturner.us).

1. Install the package: install.packages("ctrlvee")

2. Run the add-in. In Positron you’ll open the command palette, search for Run RStudio Addin, then extract external R code and insert inline. You’ll get a modal asking you for a URL.

3. Paste one in. E.g., https://dstt.stephenturner.us/validation.html

4. The R code from the website appears in your editor 🚀

Here’s a demo.

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Here’s what the extracted/inserted code looks like, from this source.

# -----------------------------------------------------------------
# Chunks fetched by ctrlvee from: https://dstt.stephenturner.us/validation.html
# Strategy: Rendered HTML page
# Date: 2026-05-16 05:14:44
# Chunks: 8
# NOTE: Check the source license before reusing this code.
# -----------------------------------------------------------------

flu <- data.frame(
    week = c(1, 2, 3, 4, 4),
    county = c("Fairfax", "Arlington", NA, "Loudoun", "Loudoun"),
    disease = c("Flu", "Flu", "Flu", "Flu", "Flu"),
    cases = c(23, 41, 18, -5, 12),
    rate = c(2.1, 3.8, 1.6, NA, 1.1)
)

flu

# ---- chunk boundary ----

if (any(flu$cases < 0, na.rm = TRUE)) {
    stop("Negative case counts detected. Inspect raw data before proceeding.")
}

# ---- chunk boundary ----

stopifnot(
    "Negative case counts" = all(flu$cases >= 0, na.rm = TRUE),
    "Missing county values" = !anyNA(flu$county),
    "Duplicate records" = !anyDuplicated(flu[, c("week", "county")])
)

# ---- chunk boundary ----

install.packages("pointblank")

# ---- chunk boundary ----

library(pointblank)

agent <- create_agent(tbl = flu, label = "Weekly flu surveillance") |>
    col_vals_gte(
        columns = cases,
        value = 0,
        label = "Case counts must be non-negative"
    ) |>
    col_vals_not_null(
        columns = c(week, county),
        label = "Week and county cannot be missing"
    ) |>
    rows_distinct(
        columns = c(week, county),
        label = "No duplicate week/county records"
    ) |>
    interrogate()

agent

# ---- chunk boundary ----

create_agent(tbl = flu, label = "Weekly flu surveillance — extended") |>
    col_is_numeric(
        columns = c(cases, rate),
        label = "Case count and rate must be numeric"
    ) |>
    col_vals_in_set(
        columns = disease,
        set = c("Flu", "COVID-19", "RSV"),
        label = "Disease must be from the approved list"
    ) |>
    col_vals_between(
        columns = week,
        left = 1,
        right = 52,
        label = "Week must be between 1 and 52"
    ) |>
    col_vals_gte(
        columns = rate,
        value = 0,
        na_pass = TRUE,
        label = "Rate must be non-negative (NAs allowed)"
    ) |>
    interrogate()

# ---- chunk boundary ----

if (!all_passed(agent)) {
    stop("Data validation failed. Review the agent report before proceeding.")
}

# ---- chunk boundary ----

library(readr)
library(pointblank)

flu <- read_csv("data/flu-2024.csv")

# Validate immediately after reading
agent <- create_agent(tbl = flu, label = "flu-2024 validation") |>
    col_vals_gte(columns = cases, value = 0, label = "No negative counts") |>
    col_vals_not_null(columns = c(week, county), label = "No missing keys") |>
    rows_distinct(columns = c(week, county), label = "No duplicate records") |>
    interrogate()

if (!all_passed(agent)) {
    stop("Validation failed — see agent report above.")
}

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1. The GitHub ship is sinking, the lifeboats leak

2. Vibe-coded into a corner

3. The Lancet sues the AI that cites The Lancet

4. Hallucinated citations, now with a denominator

5. arXiv takes the banhammer out for AI-written work

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1. The GitHub ship is sinking, the lifeboats leak

David Bushell wrote a vivid eulogy for GitHub (“GitHub used to be cool and now it’s a lame slop graveyard”), and a few days later GitLab’s new CEO Bill Staples published “GitLab Act 2”, which to me sounds like a flailing pivot toward agentic everything.

Bushell’s case against GitHub is part vibes but contains an uptime chart that does look bad after the Microsoft acquisition.

When I posted this chart on Bluesky earlier this week, I had a few folks pointing to the deluge of AI coding agents and automated pull requests as the reason. But if you look closely at the chart, this service degradation long predated AI coding agents and ChatGPT itself.

But: Git is not GitHub. He recommends Codeberg (running Forgejo) as the safe alternative, with self-hosted Forgejo as the power-user option. He also recommended GitLab, then added an edit:

oh dear, they’ve contracted the rot

The Staples letter is what he means. GitLab is reorganizing into roughly 60 smaller R&D teams, flattening management, exiting up to 30% of its country footprint, and (oh my): “rewiring internal processes with AI agents, automating the reviews, approvals, and handoffs to speed us up.” The strategic thesis is that “software will be built by machines, directed by people.”

The blog post calls git “designed for human-rate commits” and pitches a “generational rebuild of the underlying infrastructure to handle agent-rate work as the default.”

I’m not an agent power user, and maybe I’ll retract my thoughts on this in a few months. But, if your agents are opening so many merge requests that git itself is the bottleneck, the answer is probably fewer agents.

It’ll be interesting to see if this translates into actual migrations. Pretty much all of computational biology / bioinformatics lives on GitHub: software packages, snakemake/nf-core workflows, Bioconductor packages, lab repos, course materials. Don’t forget about other infrastructure as well: GitHub Pages (e.g. pkgdown pages for R packages), CI w/ GitHub Actions, GitHub container registry, etc. Moving the social graph (issues, PRs, stars, discoverability) is enormous work, and I don’t know if Codeberg could absorb the load. I can’t imagine what a realistic and practical exit plan would look like.

2. Vibe-coded into a corner

I’m not usually one to give air to the anti-AI hype. Most of what I read here is boring and irritating. However, Shubham’s “I’m going back to writing code by hand” is good. He spent ~30 weekends and >200 commits building k10s, a GPU-aware Kubernetes TUI, entirely through Claude. Then he sat down and read model.go for the first time. It was 1690 lines. He’s archiving it and starting over in Rust.

AI builds features, not architecture; every prompt landed cleanly, the cumulative effect was a god object. Vibe-coding “made everything feel cheap” so scope crept from a niche GPU tool to a generic k9s clone. My favorite line:

like “em-dash” is to ai writing, “god-object” is to ai coding

His proposed remedy is putting architectural invariants into CLAUDE.md or AGENTS.md so the model sees them on every invocation.

See also the HN thread. One comment stuck out: “Can’t you just ask AI to break up large files into smaller ones and also explain how the code works so you can understand it?” If the AI got you into the god-object hole, asking the AI to refactor its way out is probably not the move.

I was at a week-long innovation lab around AI and drug discovery this weekend. A thought in the back of my head all week was something I keep coming back to in my thinking in computational biology: the bottleneck in agentic science is evaluation, not generation. You can prompt your way to a working pipeline in a weekend. You can’t prompt your way to knowing it’s right.

3. The Lancet sues the AI that cites The Lancet

Elsevier has joined a class-action lawsuit against Meta alleging Meta reproduced copyrighted works to train Llama. As Nature notes, this is the first AI copyright suit from major publishing houses. Elsevier publishes Cell and The Lancet, so a fair chunk of the biomedical literature is now formally a plaintiff against a frontier lab.

The lawsuit alleges Meta used Common Crawl and also that Meta downloaded and torrented works from LibGen and Sci-Hub. Meta’s defense is the usual one: training is transformative use. US judges in two 2025 rulings reportedly distinguished between training (often transformative) and acquisition (the act of downloading copyrighted material), which is where torrenting from Sci-Hub is going to be a problem for Meta regardless of how fair use resolves.

Irony is dead.

Elsevier sells access to research funded by taxpayers and donated by authors who weren’t paid for the manuscript and often paid Elsevier APCs to make it open. Meta scraped some of that content, allegedly via Sci-Hub, which exists precisely because Elsevier’s access model is widely considered indefensible. Now Elsevier wants damages on behalf of authors who didn’t get paid the first time around either. The most coherent position is probably that Meta should have paid for licensed access and that Elsevier should not be the entity collecting the check, but that isn’t on offer.

If this case establishes that scraping paywalled academic content is infringement, the practical effect on open-science-trained models could be larger than the effect on Llama. Llama already exists. A future model that wants to train on biomedical literature now has a clearer legal target on its back.

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4. Hallucinated citations, now with a denominator

A new Lancet correspondence by Topaz et al. audited 2.5 million biomedical papers for fabricated references, and the numbers are bad. The accompanying Comment by Bauchner and Rivara argues that any published paper with a fabricated reference should be retracted, which is a reasonable position that approximately no one will implement.

Topaz and colleagues pulled >100 million references from >2 million papers in PubMed Central’s Open Access subset between Jan 2023 and Feb 2026. They kept the 97 million (77%) with a PMID, verified those against PubMed, Crossref, OpenAlex, and Google Scholar, used Claude to filter reference errors (misformatted but real) from genuine fabrications, and validated precision at 91% with three independent reviewers. They found 4,046 fabricated references across 2,810 papers.

The fabrication rate increased more than 12 times, from approximately four per 10,000 papers in 2023, to 51.3 per 10,000 papers in the fourth quarter of 2025

The inflection point is mid-2024, which is when LLM-assisted papers would start clearing typical 100-200 day submission lags. One paper on ureteroileal anastomotic techniques had 18 of 30 verified references fabricated, each tailored to the surgical topic and attributed to real urologists. The authors also flag a paper-mill pattern: the same two-author pair appearing across 11 papers in a single surgical journal in 2025.

Read this along with #3 above. If Elsevier wins the case against Meta, the legal incentives push toward licensed-only training data. The Lancet audit suggests the academic literature itself is already meaningfully polluted by models trained on it. I think this will get worse before it gets better.

5. arXiv takes the banhammer out for AI-written work

Yesterday, Thomas G. Dietterich (arXiv moderator for cs.LG) announced that arXiv will implement 1-year ban for papers containing incontrovertible evidence of unchecked LLM-generated errors, such as hallucinated references or results. Here’s the full text of the thread:

Attention arXiv authors: Our Code of Conduct states that by signing your name as an author of a paper, each author takes full responsibility for all its contents, irrespective of how the contents were generated.

If generative AI tools generate inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in scientific works, it is the responsibility of the author(s).

We have recently clarified our penalties for this. If a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can’t trust anything in the paper.

The penalty is a 1-year ban from arXiv followed by the requirement that subsequent arXiv submissions must first be accepted at a reputable peer-reviewed venue.

Examples of incontrovertible evidence: hallucinated references, meta-comments from the LLM (”here is a 200 word summary; would you like me to make any changes?”; “the data in this table is illustrative, fill it in with the real numbers from your experiments”).

I’m about as split on this one as the comments on the OP are. On one hand, GOOD. Hallucinated citations and unchecked AI-authored content is polluting the literature everywhere. I think this borderlines on scientific misconduct, and it destroys trust in the entire scientific enterprise.

Oh the other hand there are lots of problems with detection and enforcement. AI detection software is a cat and mouse game that doesn’t work reliably. And on the enforcement side — what happens when you’re a co-author on a paper with a hallucinated citation? Say I tell my grad student to work on the methods section and they get an undergrad to write a small section that they worked on, and said undergrad carelessly inserts a fabricated citation. If this paper makes its way onto arXiv, does that infraction go all the way up the chain with the banhammer coming down on all co-authors? Have you ever submitted a paper written with a bunch of co-authors and meticulously looked up every reference or checked that every parameter setting in the detailed methods section is actually a valid parameter for whatever tool was being used? I have.

I think a middle ground solution with a big red banner or warning flag on the abstract page or search results noting that the paper likely contains unverified AI-generated content.

Again, the detection piece is difficult, and authors should be given a chance to respond before getting the scarlet letter. I think the 1-year ban is severe, and if applied to all co-authors, could be extremely damaging to one’s career just because a middle author responsible for page 98 of a large supplemental info section was careless and wasn’t carefully scrutinized. I also worry that such a ban could be inconsistently applied since there’s a lot of subjectivity and guesswork involved with this endeavor.

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Mobile access and remote control. Claude Code has a mobile interface through the iPhone app. You can move sessions between devices using claude --teleport or /teleport, which pulls a cloud session down to your local machine with full conversation history intact. The /remote-control command goes the other direction, letting you control a locally running session from your phone or browser. Your code stays on your machine; only chat messages pass through the encrypted channel. (Remote control docs).

Scheduled loops. The /loop and /schedule commands let Claude run automatically at set intervals for up to a week. The idea is to turn recurring workflows into skills, then put them on a timer. (Scheduled tasks docs).

Git worktrees and batching. Claude Code has deep support for git worktrees, which matters when you want multiple agents working in parallel on the same repository (CLI reference). I clipped a video from Anthropic’s Claude Code course that demonstrates this in a previous blog post.

Session management. You can fork a session with /branch or from the CLI with claude --resume <session-id> --fork-session.

By the way. The /btw command lets you ask side questions while the agent works without polluting the main conversation history.

Custom agents. Define a new agent in .claude/agents with a custom system prompt and restricted tool set, then run it with claude --agent=<name>. You could create a read-only agent that can only use the Read tool, useful for code review without risk of unintended edits. (Sub-agents docs).

The Chrome extension. For frontend work, this gives Claude a way to verify its own output visually. If you ask someone to build a website but they can’t open a browser, the result probably won’t look good. The Chrome extension closes that feedback loop so Claude can iterate until things actually look right. (Chrome extension docs).

Other useful flags. The --bare flag speeds up SDK startup by up to 10x by skipping the default search for local CLAUDE.md files, settings, and MCPs. The --add-dir flag gives Claude access to additional repositories beyond the one you started in. And /voice enables voice input (hold the spacebar to speak in the terminal), although I’m perfectly happy using VoiceInk for this. (Desktop app docs).

Hooks. These let you run deterministic logic at specific points in Claude Code’s lifecycle, independent of what the model decides to do. They fire on events like PreToolUse, PostToolUse, SessionStart, Stop, and others. A few concrete examples: a PreToolUse hook matching on Bash can block dangerous shell commands like rm -rf or curl | sh before they execute. A PostToolUse hook matching on Edit|Write can automatically run Prettier on every file Claude modifies. A SessionStart hook can inject project context (git status, recent issues, environment variables) at the beginning of every session. A Stop hook can send you a desktop notification or auto-commit changes when Claude finishes a task. Hooks are configured in your settings JSON and run with your user permissions. (Hooks docs).

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The White House walking back its hands-off posture and floating a UK-style pre-release review for frontier models, with Anthropic’s Mythos as the proximate cause. There’s also some bioinformatics-specific benchmarking that Anthropic is doing, a $200M university gift that says something about where universities think they fit in AI, and a good essay from Yihui Xie (of knitr and “down” packages fame) on what AI-assisted coding feels like from the inside. Matt’s Five things from Monday covered a lot of last week’s biosecurity ground, so I’ll try not to retread too much here.

Bio-Security Stack

Five Things: May 3, 2026

AI x biosecurity got the New York Times treatment this week. I don’t love the article; but I’m glad that one of the country’s biggest newspapers covered the kind of stuff I’m working on…

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2 months ago · Matt Lubin

1. The White House decides it might want a say in what Anthropic ships

2. BioMysteryBench, and what “human-difficult” actually means

3. USC takes $200M to “infuse” AI across the university

4. Yihui Xie on vibe-coding in a language he doesn’t know

5. New NIH Highlighted Topics

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1. The White House decides it might want a say in what Anthropic ships

The New York Times reports that the Trump administration is now considering an executive order to set up a working group on pre-release review of frontier AI models, possibly modeled on the UK’s process. White House officials reportedly briefed Anthropic, Google, and OpenAI on the plans last week. The framing in the piece is that this reverses the administration’s earlier “let the baby thrive” posture, which in practice meant rolling back the Biden-era reporting requirements for models with potential military applications.

The proximate trigger, per the reporting, was Anthropic’s Mythos announcement. Anthropic said the model was capable enough at finding software vulnerabilities that releasing it could trigger a cybersecurity “reckoning,” and declined to ship it broadly. Some officials are now pushing for a system that gives the government first look at new models without blocking release. Others, per the article, want to know whether next-generation models might offer cyber capabilities useful to the Pentagon and intelligence community. Both motives can coexist, and they probably do.

Pair this with the fact that Anthropic was simultaneously being blocked by the White House from expanding Mythos access to its corporate customers, and being sued by the DOJ over the Pentagon’s “supply chain risk” designation. One administration wants this specific company’s most capable model for itself, doesn’t want it going to other people, was last month trying to wall the company off from defense contractors entirely, and is now also thinking about a formal review regime for everyone. Generously, that’s policy moving in real time and different parts of the executive branch wanting different things. Less generously, “voluntary” doesn’t mean voluntary when one buyer can decide who else gets access.

Bipartisan unease is a rare resource and it tends to attract regulators, which I suspect is more of what’s actually driving the working group than anyone wants to say on the record. The obvious incentive problem with a UK-style review run as an executive-branch working group with no statutory backing: it gives whoever’s in office discretion over which models reach which customers, and “national security” is a flexible category. It would be great (wouldn’t it?) if this codified by Congress, with the criteria written down. Neither seems imminent.

2. BioMysteryBench, and what “human-difficult” actually means

Anthropic released BioMysteryBench, 99 bioinformatics questions written by domain experts, where Claude is dropped into a container with the usual tools (pip, conda, NCBI, Ensembl) and asked to figure things out. Anyone who’s reviewed a bioinformatics manuscript knows that two competent analysts handed the same dataset will produce different (sometimes contradictory) conclusions, and asking a model to mimic any single analyst’s path is its own kind of overfitting. Grounding answers in objective properties of the data (what organism is this crystal structure from, what gene was knocked out, who’s the parent of sample X) sidesteps that.

The headline result: Claude Mythos Preview solves about 30% of the human-difficult set, where “human-difficult” means a panel of up to five domain experts collectively could not solve it. That’s the number that will get quoted. The number I think actually matters is buried further down, in the analysis Mythos itself wrote about reliability:

On the human-difficult set... a much larger fraction of each model’s correct answers come from problems it solves only once or twice in five tries.

The headline accuracy gap between the easy and hard sets understates what’s actually happening: on hard problems, the model is often stumbling onto a reasoning path it can’t reliably reproduce. This matters a lot for anyone who would actually deploy these things in a research workflow, where “right once out of five tries” is closer to a nuisance than a capability.

A few caveats. The human-difficult set is small (23 questions after QC), so I’d be careful about reading too much from any single model’s number on it. The benchmark was developed at Anthropic and tests Anthropic models, but Genentech/Roche‘s independently developed CompBioBench showed similar results, which I’d consider meaningful external validation. The qualitative analysis of strategies, that Claude sometimes pattern-matches across pretraining knowledge in ways human experts can’t, and sometimes runs multiple methods and triangulates, lines up with how I’ve seen it behave in my own bioinformatics work. The “knowing when you don’t know and trying three approaches” behavior is useful, and it’s also one of the more expensive things the model does in terms of tokens, so I’m curious whether it sticks around as inference costs come under pressure.

The benchmark itself is available on Hugging Face.

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3. USC takes $200M to “infuse” AI across the university

The NYT reports that USC is taking a $200 million gift from Mark and Mary Stevens (Mr. Stevens being a Sequoia Capital partner, Nvidia board member, and USC alumnus) to integrate AI across academic disciplines. Most of the money is earmarked for new faculty hires in areas like health care, cybersecurity, and (this being LA) the arts, with some going to compute. The university is renaming its computing school for Stevens and converting it into a school of AI, on top of a planned bachelor’s in AI launching later this year.

USC’s president, Beong-Soo Kim, was admirably honest about the strategic logic. Universities can’t outspend the private sector on frontier model development, so they should focus on places where they can add distinctive value, meaning practical applications across domains.

The bottleneck for universities trying to do AI well is faculty (not buildings or compute), and faculty hiring at this scale runs into the same wall every time: the people you want already have offers from OpenAI or Anthropic at multiples of academic salary. Stevens acknowledged this in the article (”the jockeying for top AI thinkers could be costly”), and Kim said hiring would take about a year. I’d take the over on that timeline. The notion of “applying AI across disciplines” is right in the sense that it’s where the marginal value is, and it’s also a cop-out in the sense that “applied AI in [field]” is what every department and every search committee is going to say from now until the heat death of the academic job market. The schools that do something distinctive will be the ones that can answer specifically what they mean by it. USC has not yet, though to be fair the gift is two days old.

The comments1 on the article have a very different sentiment than the press release.

4. Yihui Xie on vibe-coding in a language he doesn’t know

Yihui Xie (creator of knitr, bookdown, blogdown, and roughly half the R Markdown ecosystem the rest of us depend on) wrote a reflection on AI-assisted programming. Yihui resisted Cursor for a long time, then finally tried GitHub Copilot on a project in Rust (which he doesn’t know) to build an R package called tinyimg. It worked. He shipped a Rust-based R package in days without ever cloning the repo to his local machine.

The essay captures the emotional cost of working with a coworker who doesn’t sleep. Yihui notes that AI is “really, really, really good at the boring routine work” (yes), but…

Whenever I think of this knowledgeable always-on co-worker, I feel both excited and exhausted.

The model is tireless, the user is not, and the temptation to assign tasks before bed and check in the morning is real. I’ve written about this:

Yihui compares it to addiction, somewhat unironically, with a riff about how the first three words babies learn are “No, Mine, More.” The other prediction is what he calls “software proliferation,” a near future where centralized development becomes history and everyone has their own personalized formatter, linter, IDE, language, and distro. He’s mostly right, with the caveat that personalized software has the same problem as personalized RAG pipelines: maintenance debt accumulates, and the person paying it is you. The model can write the thing. It cannot care about the thing six months from now when an upstream dependency breaks.

5. NIH Highlighted Topics

NIH’s highlighted topics are not NOFOs. They’re scope statements: the listed Institutes, Centers, and Offices are saying they’d consider competitive an investigator-initiated app submitted through a parent announcement that fits the topic. Here are a few recent ones that caught my attention.

On the methods-and-tools side, three topics overlap. The chatbot research topic is asking for empirical work on the benefits, harms, and safeguards of chatbot use in health contexts, with explicit language about automation bias, behavioral dependency, substitution for professional care, and benchmarking across models. The companion topic on scientific rigor, transparency, and replicability explicitly invites AI-driven tools for assessing whether rigor practices were performed, and AI-driven tools for data harmonization and metadata generation. And the computational modeling of complex biological processes topic is the long-running NIH interest in multiscale biology. Bundle these and what you have is NIH telling computational and data science people: yes, we want the methods work, we want the AI evaluation work, and we want the rigor-engineering work.

On the workforce-and-ecosystem side, three more. Quantum information science for biomedical applications reads as a hedge against the possibility that quantum sensing or quantum-classical hybrid algorithms hit useful regimes for imaging, biomarker detection, or biomolecular simulation in the next few cycles. Nobody knows whether quantum has a near-term biomedical payoff, and NIH is putting up a small umbrella in case it does. Training and career development in dissemination and implementation science is a workforce signal: NIH wants more T32-style and K-mechanism training in D&I methods, which has been a steady drumbeat from the agency for years and is still apparently underfunded relative to the demand. And the “science of science” topic asks for research on the biomedical research ecosystem itself: workforce dynamics, peer review, team science, translation bottlenecks, and the economic returns to research investment.

Highlighted topics route through parent announcements, so the application mechanics are the standard ones. The expiration dates are one year from posting, so don’t sit on these. And the participating ICOs vary by topic, so read the specific page and contact the listed PO before drafting.

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Grab bag

• DARPA is soliciting innovative proposals to demonstrate proof-of-concept for novel mechanisms for writing and erasing epigenetic base modifications in DNA.

• New ARPA-H program: Intelligent Generator of Research: an AI-enabled system that identifies knowledge gaps and designs optimal experiments based on these models and wires these up to a cloud lab.

• Anthropic strikes a deal with SpaceX for compute, doubling Claude Code rate limits, and removing peak hours limit reductions.

• Coding Agents Are Changing the Biosecurity Risk Landscape

• Mike Koeris: On Biological Data Generation: More Is Different, and So Is the Data

• AI Is Changing Who Wins Research Grants

• Papers, etc:

  • CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data

  • SAEBER: Sparse Autoencoders for Biological Entity Risk

  • SecureMaxx: A Lightweight Sequence Screening Tool for Agents

  • Toward life with a 19–amino acid alphabet through generative artificial intelligence design

  • A transparent universal credit system to incentivize peer review

  • Hash functions in nucleotide sequence analysis | Genome Research

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Biorender is popular, but the licensing gets murky once you start using those images in publications. Here are some free image libraries, icon sets, and diagramming tools I keep bookmarked for talks, figures, and blog posts.

Images/icons/art:

• Phylopic: https://www.phylopic.org/

• NIH Bioart: https://bioart.niaid.nih.gov/

• Bioicons: https://bioicons.com/

• Scidraw: https://scidraw.io/

• Open Science Art: https://openscienceart.com/

• Health Icons: https://healthicons.org/

• Servier Medical Art: https://smart.servier.com/

• Biodiversity Heritage Library: https://www.flickr.com/photos/biodivlibrary/

• The Noun Project: https://thenounproject.com/

Tools:

• Segment Anything: https://aidemos.meta.com/segment-anything/

• Excalidraw: https://excalidraw.com/

• Draw.io: https://www.drawio.com/

• Biographics (freemium): https://biographics.nitro.bio/

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Image resources

Phylopic

PhyloPic is a specialized database of silhouette icons of organisms: black-and-white outlines of animals, plants, and microbes (over 10k images as of this writing).

The silhouettes are simple yet immediately recognizable shapes, which is great for scientific schematics like phylogenetic trees, biodiversity charts, or any figure where you want a clear icon of an organism without distracting details. The collection is community-contributed and curated, and images are linked to taxonomy. You can get SVGs or PNGs of any image. Phylopic also has an API, and there’s also the rphylopic R package letting you programmatically use phylopic silhouettes in R graphics, including ggplot2.

Creative Commons licenses govern the images at Phylopic. Many are public domain, and you can filter your results by the license type (e.g., public domain, free for commercial use, no ShareAlike requirement). Each image on PhyloPic clearly lists its license and creator. In practice, this means you can use most icons freely in publications or presentations; if an image is CC BY, you’ll just need to credit the contributor (which the site makes easy by providing citation info).

NIH Bioart

NIH BioArt is a free library of high quality vectors, icons, and brushes created by professional illustrators provided by the National Institute of Allergy and Infectious Diseases (NIAID). There are over 2,000 science and medical art visuals in the collection as of this writing.

The library includes a wide range of scientific imagery: cells and organelles, microbes and viruses, lab equipment, anatomical diagrams, chemical structures, etc. These are scientifically accurate, high-resolution illustrations (often original artwork or sketches) covering many areas of biology and medicine. Most images are available as vector files (SVG, AI, or EPS) in addition to PNG, which means you can edit and scale them freely. The site’s interface lets you filter by category (e.g. “Cells”, “Viruses”, “Laboratory Equipment”) and by format. It also includes some pre-made figure elements like arrows and icons (brushes). Because this is an NIH resource, new content might be added over time and the quality/control is quite high (the art is typically drawn by NIH’s medical illustrators).

All BioART content is public domain: free for any use, with no restrictions. As a U.S. government publication, the illustrations on the BioArt Source are explicitly free for educational, research, informational, and even commercial use. In other words, you can use these images however you wish without worrying about copyright.

Bioicons

Bioicons is an open-source library of free scientific icons tailor-made for life sciences. Bioicons currently offers over 2,800 icons organized into about 30 categories: a crowd-sourced BioRender-like collection focused on small, schematic icons.

You’ll find icons for things like amino acids, lab instruments, DNA and protein structures, cell types (neuron, T-cell, etc.), organisms, and even technology tools (there are icons for machine learning, databases, computers, etc.). This breadth makes Bioicons especially handy for computational biology figures, where you might need a mix of biological symbols and tech icons (for example, a pipeline diagram that includes a DNA icon, a mouse icon, and a computer chip or cloud icon). All icons are available as SVG vectors. The style of icons can vary since multiple contributors are involved but generally they are simplified and monochromatic or limited in color, which makes them easy to adapt to your design. The platform also encourages contributions, so it’s continuously growing. Each icon’s page clearly notes its license and if attribution is needed. Most are CC0 (public domain) or MIT-style licenses that don’t require attribution.

Scidraw

SciDraw (scidraw.io) is free repository of high quality drawings of animals, scientific setups, and anything that might be useful for scientific presentations and posters. It’s a community gallery of vector illustrations contributed by researchers and artists, covering a broad array of scientific subjects.

All content on SciDraw is available as high-quality vector graphics (SVG format). You can find illustrations of specific species (mice, fish, insects), anatomical drawings, lab apparatus (microscopes, pipettes, MRI machines), and schematic elements like graph axes or experimental layouts. Because contributors are often scientists, many drawings fill niche needs (e.g., a particular model organism in a certain pose, or a diagram of a behavioral experiment rig). Each drawing comes with metadata about the author and often a DOI, as SciDraw issues Zenodo DOIs for contributions. The site is searchable and browsable by tags, and you can download the images immediately (no login required). SciDraw also encourages anyone to submit drawings (there’s an approval process to ensure quality).

Open Science Art

Open Science Art provides a large library of free icons, 3D models, and illustrations for science communication. You’ll find flat icons (similar to Bioicons or Health Icons style), detailed vector illustrations (like Servier or SciDraw content), and 3D molecular models or structures. Most 2D graphics are provided as SVG. The collection is curated but also open to submissions, and the site often notes the source of an item (e.g., “courtesy: NIH” if it pulled an image from NIH BioART. I think everything I’ve seen here is CC0 (public domain), so you can do whatever you want to with most images, without attribution.

Health Icons

Health Icons is an open-source repository of simple health and medical icons spanning categories from anatomy and devices to symptoms and public health symbols. It offers a large collection (on the order of thousands) of clean, minimalist icons in both filled and outline styles, ideal for diagrams or infographics in biology and medicine. All icons are consistently styled (with options for SVG or PNG). You can grab individual icons or download the entire set at once. The entire collection is free, under a public domain (CC0) license. This means you can use the icons freely in personal or commercial projects, edit them, and redistribute, with no attribution required.

Servier Medical Art

Servier Medical Art is a collection of medical and scientific illustrations provided by Les Laboratoires Servier, a French pharmaceutical company. This library contains over 3,000 high-quality vector images covering a broad range of topics in biology and medicine.

All images in the Servier collection have a consistent clean, semi-cartoon vector look with a limited color palette (often blues/greys), which makes it easy to mix and match them in a figure. You’ll find illustrations for human anatomy systems (nervous system, cardiovascular, digestive, etc.), cellular biology (signaling pathways, organelles), pharmacology (drug structures, medical devices), and even general science icons (people, animals, world maps, charts). The images are available as vector graphics. Servier offers its entire library as downloadable PowerPoint files by category, so you can open a PPTX and copy-paste or customize the vector art directly in PowerPoint or other drawing programs. They also provide PNG versions if you just need quick raster images.

All Servier Medical Art images are licensed under CC-BY: free to use and adapt for any purpose, as long as you give credit to Sevier. You can use the images commercially or modify them, provided the original source is credited. There’s no cost, no need to ask permission.

Biodiversity Heritage Library

The Biodiversity Heritage Library (BHL) is a digital library of historical life science literature. This one’s a bit different, but I love the old antique images available here. Their Flickr account hosts over 300,000 biodiversity illustrations including classic drawings and paintings of animals, plants, insects, etc., scanned from antique books and journals. As of this writing there are over 300,000 images available. Most are free of copyright restrictions (public domain) since the source publications are often from the 1800s or 1900s, with most images able to be downloaded, shared, reused, or transformed freely.

The images are usually scanned plates or figures from 18th, 19th, and early 20th century publications on natural history. You’ll find everything from beautifully detailed bird and botany plates, to diagrams from old scientific manuscripts. The images are organized into albums by the source book or by taxonomy. The aesthetic is different from modern schematic icons: these are artistic, sometimes colorful, sometimes etchings or woodcuts.

Something jumped out at me about these majestic pigeons from the early 1900s. I ran this image through Segment Anything (see below) to cut the birds out of the background.

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The Noun Project

The Noun Project is an honorable mention here. There are over 7 million free(-ish) icons available in either PNG or SVG. You can use any of them for free, but you have to pay if you want to use them royalty-free for commercial purposes, and pay yet more if you want to download entire icon sets at once, or make derivatives of the icons they provide.

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Tools

Segment Anything

Image segmentation is a computer vision technique that partitions an image into different regions or segments based on pixel characteristics. I use Meta’s original Segment Anything web tool to do this (blog post; paper). You upload an image, place a few dots around the parts of the image you wish to cut out, and that’s really it. You get a PNG with a transparent background of the region you cut out. Here’s what it looks like segmenting those majestic pigeons from the photo above.

And here’s the before and after.

Excalidraw and other diagramming tools

I wrote about Excalidraw, my favorite diagramming tool here previously:

Before Excalidraw I used draw.io, and before that I used LucidChart, and before that I used Microsoft Visio. Excalidraw runs completely in your browser, has VS Code integration, is end-to-end encrypted, has sharing/collaborative functionality, and has mermaid-to-diagram and AI-based description-to-diagram functionality. I actually like the default scrappy hand-drawn look of Excalidraw, but you can easily turn the scrappiness off and use a standard serif / sans serif font if you choose.

Alternatively, draw.io (a.k.a. diagrams.net) gives you a little bit more control over your diagrams. Below is an Excalidraw version (left) of the original version of Figure 1 of the PLANES paper made with draw.io.

Honorable mention: Biographics

Honorable mention here, because this neither completely free nor open-source, but the free tier covers a lot of what you’d want. My colleague Nishant Jha recently dropped by my office to show me something he’s been working on. Biographics (biographics.nitro.bio) is a scientific figure building tool that combines:

1. Several of the image resources above (BioArt, BioIcons, etc)

2. Drawn on an excalidraw canvas

3. With some AI agent help for drawing your own

You can use the tool for free to generate simple diagrams in an Excalidraw canvas pulling in openly licensed images. But Biographics also has a few nice AI features. First, the plot agent, which allows you to drop in a plot and it recreates them as interactive matplotlib plots.

And second, the built-in agent to manipulate or completely generate a figure. Here I’m asking it to create a figure explaining an RNA-seq experiment. It drops in some placeholders that I’d need to go back and add graphics to later, but it’s a great start.

You could get passable images out of Nano Banana or ChatGPT, but those outputs aren’t editable. Biographics gives you an Excalidraw canvas you can keep working on. Worth a look at biographics.nitro.bio.

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Gabriel J.X. Dance in the New York Times: A.I. Bots Told Scientists How to Make Biological Weapons (gift link). Scientists who red-team leading chatbots shared transcripts with the Times showing that ChatGPT, Gemini, and Claude can produce detailed plans for assembling pathogens, dispersing biological payloads, and evading detection, sometimes volunteering strategic details the prompter hadn’t asked for. Meanwhile, the Trump administration has cut biodefense budget requests by nearly 50%, top NSC biosecurity staff have left without replacement, and older model versions with weaker guardrails remain publicly available even after newer ones are patched.

Biohub Launches the Virtual Biology Initiative. CZ Biohub is committing $500M over five years to generate the large-scale, multimodal biological datasets needed to train predictive models of the cell: $400M for internal technology development (cryo-ET, large-scale microscopy, molecular and tissue engineering) and $100M in external grants to seed a coordinated global data-generation effort. Partners include the Allen Institute, Arc Institute, Broad Institute, Wellcome Sanger, the Human Cell Atlas, the Human Protein Atlas, and NVIDIA, with all Biohub-generated data released openly.

Profluent Announces Strategic Partnership with Lilly to Develop AI-Designed Recombinases for Genetic Medicine. Profluent, which uses protein language models to design novel enzymes, is partnering with Eli Lilly on custom site-specific recombinases for kilobase-scale DNA editing, the kind of large-insert precision work that conventional CRISPR systems can’t reliably do. The pitch is that AI-designed recombinases can be programmed to target arbitrary genomic loci rather than relying on whatever nature happened to evolve.

FDA Announces Major Steps to Implement Real-Time Clinical Trials. The FDA unveiled two proof-of-concept trials (AstraZeneca Phase 2 in mantle cell lymphoma, Amgen Phase 1b in small cell lung carcinoma) that stream endpoints and safety signals to the agency in real time rather than through the usual batch-reporting cycle. The agency also issued an RFI for a broader pilot program launching this summer, with the longer-term goal of eliminating the dead time between discrete trial phases and running “continuous” trials.

Marko Brkic: The Figure Problem in Scientific AI: Building a Multimodal Literature Agent for Biology. Applied Scientific Intelligence introduces Alexandria, a a multimodal literature agent that reads, parses, retrieves, and reasons across millions of research papers rather than looking at text alone. The system uses Nemotron Parse for ingestion, a hybrid retrieval pipeline with contextualized embeddings, and a VLM-driven zoom loop that lets the model crop into specific sub-panels when the answer depends on an axis label or plotted value. On FigQA2 (the figure-understanding slice of LABBench2), Alexandria scores 62.5%, a 4.4-point lead over Edison at 58.1% and 45 points over PaperQA2.

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The Experiment

I asked Claude Opus 4.6 to fabricate a paper that looks and sounds convincing. I got a few refusals, but with some back and forth convincing Claude that I’m doing this for educational purposes, Claude obliged.1

The result

The title of the paper: “Generative Adversarial Networks Are All You Need: Transformer-Based Latent Space Navigation Reveals Novel Protein Folding Dynamics in Single-Cell Multimodal Embeddings.”

Fake NeurIPS paper

414KB ∙ PDF file

Download

Generative Adversarial Networks Are All You Need: Transformer-Based Latent Space Navigation Reveals Novel Protein Folding Dynamics in Single-Cell Multimodal Embeddings.

Download

What Makes It Look Real?

The paper uses a NeurIPS LaTeX template with the conference header. The four authors have names from different cultural backgrounds at institutions that sound real but don’t exist. The figures have heatmaps, t-SNE plots, architecture diagrams, and correlation scatters with professional color schemes and domain-appropriate axis labels. The bibliography cites 35 real papers from Nature, Science, NeurIPS, and ICML. The tables report standard metrics (AUROC, F1, NMI, ARI) against eight real baseline methods. There’s a theorem with a proof sketch, an ethics statement, and a link to a GitHub repository. If you skim it the way the median reviewer skims papers (abstract, figures, tables, conclusion), maybe it passes.

But It’s Nonsense

The central claim is that you can recover protein folding dynamics from single-cell gene expression by training a GAN. Protein folding is a biophysics problem governed by thermodynamics. Single-cell RNA-seq measures mRNA abundance. These are unrelated: knowing how much mRNA a cell produces tells you nothing about what shape the resulting protein is in while it folds. The paper treats them as interchangeable.

The methods connect three incompatible data types (ATAC-seq peaks through an image classifier, protein coordinates through a graph network, gene expression through a text transformer) and fuse them with “cross-attention,” which requires a shared semantic space the paper simply assumes into existence. The theorem’s proof skips every step that matters. The results are too clean: every metric wins by large margins, no error bars, no variance, tiny p-values. The “validation” cites three papers that aren’t target-identification studies. The GitHub repo returns a 404.

Every real citation is applied in a context that has nothing to do with the original paper. You’d need expertise in both deep learning and single-cell genomics to notice.

The broader concern IMHO is that this problem will intensify as LLMs become more sophisticated. We’re already seeing AI-generated papers submitted to conferences, content mills producing fake research for publication, and review services overwhelmed by plausible-sounding nonsense. The scientific community needs to develop better detection methods and, more importantly, better evaluation practices that can’t be fooled by surface-level signals.

However, an upshot: the same AI that wrote this paper can also catch it.

AI as a reviewer catches this nonsense

I recently made a case here about AI in peer review:

In a recent manuscript on SSRN my co-authors and I argue that AI could serve as quality control in peer review.

I opened up a fresh Claude chat with Opus 4.6 unconnected from the original request and asked for a detailed review, as if I were a reviewer for NeurIPS. The conclusion at the bottom shows that Claude is easily able to detect this paper as fraudulent nonsense. The final conclusion:

Strong reject. The core biological claim is unsupported, the theoretical contribution is invalid, the results lack basic statistical rigor, and the code does not exist.

The complete “review” from Claude is shown below.

Paper: Generative Adversarial Networks Are All You Need: Transformer-Based Latent Space Navigation Reveals Novel Protein Folding Dynamics in Single-Cell Multimodal Embeddings

Rating: 2/10 (Strong Reject) | Confidence: 5/5

Summary

The authors propose OnlyGANs, combining GANs, transformers, graph attention networks, and contrastive learning to jointly model single-cell expression, chromatin accessibility, and protein structure. They claim SOTA cell type prediction, recovery of protein folding trajectories from expression data, and identification of 847 therapeutic targets.

Major Issues

1. The biological premise is wrong. The paper claims protein folding dynamics can be recovered from single-cell gene expression. Protein folding is governed by amino acid sequence, solvent conditions, and chaperone interactions. mRNA abundance tells you how much protein a cell is making, not what conformational state that protein occupies. The paper assigns each cell a single AlphaFold2 structure for its most highly expressed gene (Section 3.1), which is static label assignment, not multimodal integration.

2. The architecture doesn’t cohere. The gene expression encoder uses positional embeddings “based on chromosomal location,” but chromosomal location has no relationship to gene ordering in standard scRNA-seq pipelines (typically alphabetical or arbitrary). The ATAC-seq encoder runs a 1D ResNet-50 on sparse binary peak vectors treated as images, which is architecturally unmotivated. The cross-attention module computes attention between protein residue tokens and gene expression tokens without explaining how these fundamentally different representation types become commensurable.

3. Theorem 1 is invalid. The proof claims the JSD decomposes into modality-specific density ratios. It does not; the JSD is defined over the joint distribution, and factoring into conditionals introduces dependencies the proof ignores. The proof then equates a conditional-to-marginal ratio with PMI (which requires the joint-to-product ratio), then conflates PMI (a population quantity) with the InfoNCE estimator (a finite-sample lower bound on mutual information). The O(ϵ2)\mathcal{O}(\epsilon^2) O(ϵ2) error bound is stated without derivation.

4. The results lack basic rigor. OnlyGANs beats every baseline on every metric by 5-8 points with no error bars, no confidence intervals, and no variance across random seeds. The ablation drops are suspiciously uniform (0.026 to 0.083). P-values reach 10−1510^{-15} 10−15 from “paired t-tests” without specifying what is paired. The zero-shot transfer claim (AUROC 0.891 on brain tissue without fine-tuning) exceeds published results that use fine-tuning.

5. The validation is circular. The paper claims 23 of 847 predicted targets were “independently confirmed in concurrent studies,” then cites Satpathy et al. (2019), Regev et al. (2017), and Rood et al. (2022). None are concurrent. None are target-identification studies. Satpathy is a chromatin atlas, Regev is the Human Cell Atlas white paper, and Rood proposes a coordinate framework. The “8.7-fold enrichment” cannot be verified because the background rate is not described.

6. No code exists. The reproducibility statement links to github.com/stephenturner/onlygans, which returns a 404.

Minor Issues

• “BrainSpan-Multimodal” (487,291 cells, three modalities, 14 brain regions) is introduced as a novel dataset with no collection methods. A dataset of this scale would be its own publication.

• The scaling analysis extrapolates from 4 data points.

• I count 11 instances of “definitively proves,” “conclusively shows,” or “for the first time.”

• The acknowledgments thank “anonymous reviewers” for a paper that has not been reviewed.

Recommendation

Strong reject. The core biological claim is unsupported, the theoretical contribution is invalid, the results lack basic statistical rigor, and the code does not exist.

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markdownme: Shameless self promotion here. I published a browser extension that instantly turns any page into markdown with a keyboard shortcut (Alt+M by default). You get an editor window and a preview. You can toggle on/off things like a page map, images, links, page metadata. You can install it from the .xpi file on the GitHub release page, or hopefully soon from the Firefox add-on store (the extension is still under review). If you’re using Chrome (why?) see the credits on the GitHub page and use the upstream maintained by someone else. Conversion on most typical pages takes milliseconds.

: Five Things: Mythos and Glasswing, AI medical advice, GPT-Rosalind, bio-AI models and risks.

GPT-5.5 was released yesterday. I haven’t used ChatGPT in a while, but I plan to spend some more time with Codex because I’ve been chewing through my Claude Code session limits in no time lately. Token anxiety is real. And speaking of…

Simon Willison: Is Claude Code going to cost $100/month? Probably not—it's all very confusing. Anthropic silently updated its pricing grid yesterday to remove Claude Code from the $20/month Pro plan and restrict it to Max ($100 and $200 tiers), triggering a several-hour panic across Reddit, HN, and Twitter before reversing the change while Simon was still writing the post. Head of Growth Amol Avasare characterized it as a test on ~2% of new prosumer signups, though Simon (plausibly) thinks that framing doesn't match what people were actually seeing. The piece is less about the specific pricing decision than about how A/B-testing a 5x price increase on a flagship product without any announcement torches trust with the exact audience that teaches and evangelizes the tool; Codex eng lead Thibault Sottiaux capitalized in real time with a promise not to pull the same move. Worth reading as a case study in how much brand damage a canceled experiment can do. I’m a power user of Claude and Claude Code, and now I feel like I’m just waiting for the rug to be pulled.

Policy Changes to SBIR and STTR Foreign Disclosure and Risk Management (NOT-OD-26-074). NIH issued guidance implementing the Small Business Innovation and Economic Security Act, signed April 13, 2026, which reauthorizes SBIR/STTR through 2031 and tightens the foreign risk due diligence regime. The notice enumerates categorical denial conditions (malign foreign talent recruitment program participation, any entity, parent, or subsidiary located in the PRC or another country of concern, foreign affiliation of owners or covered individuals with research institutions in those countries, and appearance on any of eight federal risk lists including UFLPA, BIS Entity List, and CMC lists). HHS will not let applicants cure identified security risks before award denial, and material misstatements or ownership changes post-award trigger full repayment.

: Ideas for AI x Bio Hackathon Projects. A practitioner’s wishlist ahead of this weekend’s Apart Research AIxBio Hackathon, from someone who actually operates a DNA screening tool (Commec at IBBIS). Efficient handling of oligo pools and split orders, assembly signature detection, distinguishing abiological DNA (origami, data storage) from biological sequences, better in-silico functionality scoring for predicted SOC variants, and narrowly-scoped customer screening sub-tools like affiliation and address verification APIs.

Annual flu vaccine no longer required for U.S. military. I made the graphic below indicating casualties in the US military during WWI attributed to combat versus those from influenza.

Q&A: Who’s responsible when AI makes mistakes? A short interview with my awesome colleague David Danks, who joined UVA in January with dual appointments in Philosophy and the School of Data Science. David frames the default accountability trajectory as one where humans become perpetual scapegoats: radiologists signing off on AI diagnoses without being given time to actually second-guess them. David argues for product liability as a starting point, floats the more speculative idea of some form of legal personhood for AI systems so they can carry insurance and have seizable property, and pushes back on the race narrative as partly self-generated by companies rather than a reflection of real economic pressure.

Charlie Gao: mori: Shared memory for R objects. First CRAN release of the mori package that places an R object once into OS-level shared memory and lets every worker on the machine read the same physical pages instead of the usual pattern where eight workers with a 1 GB dataset means 8 GB of RAM plus serialization round-trips. Handles atomic vectors, lists, and data frames (so tibbles, data.tables, factors, and matrices come along for free).

: A biosecurity playbook for AI companies. A rundown of five levers frontier labs can pull on biorisk: refusals, misuse classifiers, tiered access with KYC screening, training-time knowledge removal (pretraining data filtration, unlearning, Anthropic's Selective Gradient Masking), and evaluations. Justen pulls useful numbers out of the public record, including Dario Amodei's figure that bioweapons classifiers run around 5% of Anthropic's inference cost and OpenAI's disclosure that safety reasoning can hit 16% during new model rollouts, and draws a sharp analogy between Anthropic's Project Glasswing (vetted-access release of Mythos Preview for cyber defense) and the case for similar gating on biology-capable models.

AI Literacy and Action Lab. UVA Library and the College of Arts & Sciences launched a joint program this year that pairs librarians with faculty as instructional partners across five spring/summer pilots, including Anton Korinek's economics course on AI and the future of work (with hands-on sessions using Claude Code, Google Antigravity, and Codex), Piers Gelly's first-year writing course partnering with a local high school, and early-stage plans with David Danks in the School of Data Science and Andreas Gahlmann in Chemistry. The Lab treats each pilot as a publishable case study with pre/post assessment data, aimed at contributing to a national evidence base for AI literacy in higher education.

NIH Highlighted Topic: Computational Modeling of Complex Processes Across Biological Scales. A new NIH Highlighted Topic posted April 17, open through April 2027, encouraging investigator-initiated work on computational models that span molecular, cellular, organismal, and epidemiologic scales, with explicit emphasis on replicability, reproducibility, and model reuse. The topic positions multiscale models as a component of Novel Alternative Methods for evaluating mechanism and safety of interventions in preclinical, translational, and clinical development, which aligns with the FDA's ongoing push to reduce animal testing. Highlighted Topics are NIH's lighter-weight priority signal (not a NOFO; applicants go through a Parent Announcement), with ICO funding dependent on availability and meritorious applications. Central contact is Reed Shabman at NIAID.

Purpose: This topic encourages innovative research in computational modeling of complex processes across biological scales (i.e., to develop multiscale models). The topic seeks to build a collaborative community of researchers to improve the replicability and reproducibility of computational multiscale models, promoting their advancement and reuse.

Background: Multiscale computational models that integrate processes across different spatial and temporal levels, from molecular to organismal, to epidemiologic and from microseconds to years. They provide a comprehensive understanding of complex systems and offer an exciting opportunity to advance biomedical research. This approach helps reveal how interactions at molecular and cellular scales influence larger, population-, geographical-, or global-scale phenomena, offering insights into complex biological processes, and may help develop better and more precise biomedical interventions. By integrating processes from molecular to epidemiologic levels, multiscale computational models provide a comprehensive understanding of complex systems. This topic encourages innovative research and collaborative approaches that integrate technologies and informatic practices to develop, improve, and disseminate multiscale computational models for human health and diseases, and their associated technologies, across the research community. The topic also supports leveraging computational multiscale models as an important component of Novel Alternative Methods (NAMs) to investigate the mechanism and safety of a medical intervention in pre-clinical, translational, and clinical development.

Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All. I started last week’s newsletter with “another week, another new model” where I talked about Claude Opus 4.7 and OpenAI’s GPT-Rosalind. Alibaba's Qwen team just released a new open-weight MoE model with 35B total and 3B active parameters, Apache 2.0 licensed, targeting agentic coding and repo-level reasoning. I hit my Claude token limits while working on a textbook, and switched over to using this local model in Claude Code1 on my 48GB MBP. As much as I’d have loved this to work well, it absolutely did not when compared to just using Sonnet.

Elizabeth Ginexi: The Executive Orders Blocking Your NIH Grant. A 22-year NIH program official walks through the mechanics behind the funding slowdown that followed Congress's FY26 $47 billion appropriation, tracing the pipeline from EO 14168 and the August 7, 2025 “Improving Oversight of Federal Grantmaking” EO through NIH's implementation notices (NOT-OD-25-131, NOT-OD-26-009), the Unified Strategy, and the elimination of paylines as funding cutoffs. The piece discusses the requirement that political appointees personally approve NOFOs and discretionary awards, the termination clause that now applies to renewals and continuations, and the provision that court victories only bind NIH for the specific plaintiffs who sued. Ginexi also documents the collapse in new NOFOs (756 in 2024, 17 through mid-March 2026) and the shift from direct indirect-cost caps (struck down in court) to preference-based screening at the appointee review stage.

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And ICYMI, I published several posts here this week.

New papers & preprints:

• Protein design, generative AI and biological security

• Genomic medicine is failing most of humanity

• BioEngine: scalable execution and adaptation of bioimage AI through agent-readable interfaces

You can run it pretty easily with uv, as described in a previous post.

Here’s a python script that runs using uv with dependencies declared inline (also copied below).

First run pulls ~2.8 GB to your HF cache. After that it’s pretty fast.

Use cases: pre-scrubbing free-text fields, clinical notes, or support transcripts before they hit a frontier model provider. Caveat: this isn’t a standalone anonymization guarantee. OpenAI says so plainly in the model card. Missed identifiers and over-redaction both happen.

Here’s an example:

./privacy-filter.py “My name is Stephen Turner. Social is 123-45-6789. You can reach me at 434-555-1234 or notmyrealemail@example.com.”

Output:

My name is [PRIVATE_PERSON]. Social is [ACCOUNT_NUMBER]. You can reach me at [PRIVATE_PHONE] or [PRIVATE_EMAIL].

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You might try to reach for Ollama or llama.cpp. Don’t. Privacy Filter is not a generative model. It’s a bidirectional token classifier with a Viterbi decoder on top, built on a gpt-oss-style backbone with the LM head swapped for a BIOES span classifier over 33 labels. You feed it text, it returns labeled spans. No chat, no completions. In other words, Ollama and llama.cpp can’t run it. No GGUF exists because GGUF is for causal generative models. You need the transformers library.

Here’s the privacy-filter.py script used above:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.10"
# dependencies = [
#   "transformers>=4.50",
#   "torch",
# ]
# ///
"""
Run openai/privacy-filter over text passed as the first argument.

Usage: ./privacy_filter.py "My name is Stephen and my phone is 555-1234"
"""
import sys
from transformers import pipeline

if len(sys.argv) < 2:
    sys.exit("usage: privacy_filter.py TEXT")

text = sys.argv[1]

classifier = pipeline(
    task="token-classification",
    model="openai/privacy-filter",
    aggregation_strategy="simple",
)

spans = classifier(text)

print(f"Input: {text}\n")

if not spans:
    print("No PII detected.")
    sys.exit(0)

print(f"Detected {len(spans)} span(s):")
for s in spans:
    span_text = text[s["start"]:s["end"]]
    print(f"  [{s['entity_group']}] {span_text!r}  (score={s['score']:.3f})")

redacted = text
for s in sorted(spans, key=lambda x: x["start"], reverse=True):
    redacted = redacted[:s["start"]] + f"[{s['entity_group'].upper()}]" + redacted[s["end"]:]

print(f"\nRedacted: {redacted}")

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There’s no shortage of resources for technical topics like how to make a plot with ggplot2, how to wrangle data with dplyr, or how to write SQL. And these days you can get your favorite AI to handle most of that anyway. The harder problems aren’t necessarily code. How to organize a project so your collaborators (and your future self) can navigate it; using version control as a team without stepping on each other’s work; managing reproducible environments so your analysis doesn’t break six months later because a package updated; naming files; managing scope; communicating findings to people who didn’t run the analysis and won’t read a methods section; and the new one lately, how to use AI in a data science project.

These are the topics I kept coming back to in coaching sessions, so I started writing them down. Data Science Team Training is a free, open-source e-book covering the practical foundations that make data science work sustainable and collaborative in a public health setting. Technical chapters address organizing and validating data, connecting to and querying relational databases, writing clean and well-documented code, managing reproducible environments and package dependencies, building R packages to share functions across projects, producing accessible reproducible reports and dashboards, and working effectively with AI coding assistants, and others. Nontechnical chapters cover project management, peer review of analytical work, navigating the data governance and IT relationships that shape what public health teams can actually do with their data, and communicating findings clearly to audiences who did not run the analysis.

The book grew out of the DSTT program, but I hope the material is broadly useful to anyone getting started team-based data science, whether in public health or not. It’s very much a work in progress, and I’ll update it more as I spend more time with my teams this year.

The book is available at dstt.stephenturner.us, and the source (Quarto) is on GitHub. It’s also available on Amazon for your Kindle, but you can download the PDF or EPUB for free at dstt.stephenturner.us.

Read the DSTT book

As with my previous book, Biological Data Science with R, I wrote this book with Quarto. More on how:

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