Reimagining research papers as interactive and reliable AI agents
That is a new Nature paper by Jiacheng Miao, Joe R. Davis, Yaohui Zhang, Jonathan K. Pritchard, and James Zou. Here goes:
Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper’s code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent’s effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.
As I have been saying for a while now, there is much more of this coming down the pike. Via Charles Klingman.
Why Papua New Guinea is so interesting
And this leads to the most interesting part of this story: how the highlanders were so isolated for so long.
I want to acknowledge that the highlanders were not completely, hermetically sealed off from the outside world – it’s just that knowledge of outside world never actually made it to them or vice versa.
Over time, a few goods did make their way up to the highlands: pigs, about 3,000 years ago; the sweet potato, which is South American and arrived about 300 to 400 years ago; and tobacco, also American, which came the same way.
The journey the sweet potato and tobacco took to get there is a good example of how this all worked without transmitting further information. The Spanish and Portuguese brought both plants to the spice islands (in what is now Indonesia), just off the western tip of New Guinea, in the 1500s. The sultanates had been trading with the western tip of the coastal region of New Guinea for centuries. So the plants crossed over and started moving east along the coast and up into the interior. Beyond that point, there were no merchants. But when a woman married into the next clan over, she took cuttings from her family’s garden with her. Each new family then planted the same, saw that it worked, and passed it on. At that pace the sweet potato crossed the highlands in a century or two, with nobody knowing where it came from beyond the tribe beside them.
And what fascinates me is that even with the highlands as sealed off as they were, such significant goods still got in. Sweet potatoes allowed a population explosion in the highlands: they give far more calories per acre, and pigs can live off them. So in a 10,000 year sealed off universe, after the invention of agriculture, the biggest change to highland life happened in the last 500 years, which I guess isn’t a coincidence, as the sweet potato got there through the same structural forces that later brought the Australians.
Here is much more from Daniel Frank, fascinating throughout.
Thursday assorted links
1. An American at Brazil’s largest rodeo.
2. Ingeborg Bachmann centenary.
3. “Huawei Technologies Co. predicted on Wednesday that global computing demand will surge 100,000-fold by 2035, with AI agents generating over 90% of network traffic.” And a report from someone who has done AI diplomacy with China (NYT).
4. “US Gov Federal Register use distilled Qwen models in their search mode.”
Pandemic Preparation is AI Safety
I am not a big fan of the “now more than ever” post, which interprets recent events to support—surprise!—something the writer already believed. And yet…is it not obvious that AI risk increases the value of pandemic preparation?
If COVID began with a laboratory leak—plausible, though unproven—then a research accident has already killed millions. Whatever its origin, COVID demonstrated the scale of the potential damage. AI surely increases pandemic risks. If you have ever used AI to fix your washing machine, you understand the difference between reading a manual and having a tutor by your side helping to troubleshoot each problem. The same distinction matters for biological research at the laboratory bench.
Anthropic’s threat report documents five cases of researchers using its models in ways that “could support biological weapons development.” These included military-linked virus research and plans for work on pathogens with enhanced pandemic potential. Did the researchers intend to build weapons? We don’t know. But some users circumvented access controls and concealed sensitive aspects of their research.
Some safeguards appear to have worked but we don’t know the base rate. And what will happen with open-weight models and AIs not under the control of a large corporation? The safeguards also burden legitimate researchers. We should accept that AI will magnify the power of small groups, ill-intentioned or not.
Of course, AI can accelerate vaccines and treatments too. But faster science does not automatically mean faster defense. Turning discoveries into deployed vaccines and treatments requires institutions that can act quickly. We should not assume those institutions will keep pace with the technology.
That makes the recommendations in my 2025 paper, Pandemic Preparation Without Romance, more urgent (surprise!): wastewater surveillance; prediction markets; pre-developed vaccine libraries; human challenge trials planned in advance; a Pandemic Trust Fund; and temporary Operation Warp Speed–style public–private partnerships.
My paper argues that pandemic policy should not depend on unusually farsighted or wise politicians. Public tripwires and banked resources are what you build when you don’t trust the authorities to move quickly in a crisis hence the value of ideas like wastewater surveillance, vaccine libraries, and ready challenge-trial protocols. None of these technologies is highly expensive nor do they require much in the way of continued investment and attention, hence pandemic preparation without romance.
My excellent Conversation with Annie Lowrey
Here is the audio, video, and transcript. Here is the episode summary:
Annie Lowrey’s new book, The Time Tax, argues that the most regressive levy in American life is the hours each of us must spend on hold, in waiting rooms, and filling out forms a competent state would have pre-filled — roughly fifty a year on average, and far more if you’re poor. Tyler spends much of this conversation pressing the opposite case, asking whether that bureaucracy is quietly serving a useful purpose by rationing knee surgeries that don’t work, deterring fraud, or screening for immigrants with gumption.
So Tyler and Annie hash it out over an hour, covering why TANF and other welfare programs are the worst offenders, the political economy of submerged benefits like the child tax credit, whether the postal service should be zeroed out over ten years the way Denmark did, whether the government should be nudging people out of rural areas, lessons from the COVID unemployment fraud, areas where the time tax is flat instead of regressive, which private sector time taxes are most onerous, whether AI agents will pay these taxes for us within five years, the logic of easy benefits plus randomized harsh audits, the case for less federalism, whether voting should be easier or harder, how the UBI studies changed her priors, where you can find the best moss in the world, her favorite example of Brutalist architecture, tattoos as permanent ephemera, Christianity’s role in liberalism and the post-Christian right, how reporting beats tourism, the time tax of friendship, what she’ll learn next, and more.
Excerpt:
LOWREY: The postal service and Social Security’s retirement programs. Those are both pretty good. Social Security also has other programs, SSI, which is mostly used by very, very low-income seniors with disabilities or who are blind. Disability insurance is run by the Social Security Administration. The retirement program, it’s less than 1 percent overhead. It’s great. It does all the work for you.
COWEN: Isn’t our whole postal service a time tax in the aggregate? Denmark, just this year, abolished its postal service. They don’t have any. Now, I understand we’re not in a position to do exactly the same, but shouldn’t we be moving away from postal service, basically zeroing it out over the next 10 years?
LOWREY: Did they make it so that it’s all digital? Did they digitize things?
COWEN: Well, I don’t know who the “they” is. Direct mail there has gone away.
LOWREY: Oh, yes, that’s interesting. I don’t know. I think in a high inequality country such as ours, where you still have a lot of people who don’t have smartphones and don’t have computers, that’s why I would be very concerned about the postal service going away.
COWEN: That’s why we need 10 years. You want to tax people, tax in the broad sense of the term, living in rural areas, if only because it’s much harder to have government help them, right?
LOWREY: Yes.
COWEN: If rural areas don’t get mail anymore, maybe that’s a good thing.
LOWREY: I don’t think that that’s a good thing for the people in the rural areas, though. There’s always going to be people in rural areas. As a general point, do you want the government to be encouraging people to not live in rural areas?
COWEN: Absolutely, that’s me. Welcome to the show.
LOWREY: That’s feels very nanny state-ish of you, Tyler.
COWEN: I don’t want to subsidize rural areas, and we do it in so many ways, including with our farm programs.
LOWREY: We do. We really, really absolutely do.
COWEN: It gets harder to help people with government. There’s a lot more market power in rural areas, like how many supermarkets are there, how many hospitals, probably just one, if that. Let’s get everyone into the suburbs, or heaven forbid, the cities.
LOWREY: I can’t imagine a less popular policy than that.
COWEN: Absolutely.
Interesting throughout, and full of good spirit. Again, here is Annie’s very good book The Time Tax: How Government Wastes Our Time — And How to Fix It.
The correct model of AI CEO behavior
I agree with Alex that the standard model of regulatory capture does not apply here, but I think he neglects the best model we have. From my recent Free Press piece:
Recently I have been reading my review copy of Kevin Roose’s excellent forthcoming book The AGI Chronicles: The Inside Story of the Race to Create an Artificial Superintelligence. A major theme of the book—I would say the major theme—is how strong and competitive the rivalries have been between Dario, Altman, and Musk. Each believes that the others cannot be trusted to gain super-powerful AI capabilities.
Thus, when you observe these individuals and their companies jockeying for position and preeminence, it is too cynical to think it is just about the money (each of them already has plenty). The actions of each are rooted in the sincere belief that their own company would best serve the world by winning the race toward very powerful AI.
So when these CEOs plead altruism, you should actually believe them, even though obvious selfish motives also happen to align with their plans. One can reasonably argue about who ought to win the race, but I favor the outcome where all of them, including Meta and Google, roughly tie for first place, and the market remains highly competitive.
So they are selfish in a sense, but along the metric of power, where they perceive (correctly or not) “selfish and altruistic” to be quite closely correlated. Note I am using the word “power” in a general capabilities sense, you do not have to believe they intend evil coercive exercise in this context. Furthermore, it is not mainly about “capturing the regulator,” but rather winning in the market and then having an immense ability to achieve ends before the others do. So yes they are sincerely worried about a host of safety issues, but they are all the more sincerely worried about not being the ones to win the race. To the extent we wish to cite those CEOs as authorities, “who gets it is the most important thing of all” would be the takeaway lesson.
Do note that for all the talk of pause, the race to invest in compute is continuing apace and even intensifying. That is the real variable to watch. If a theory does not explain observed behavior toward compute, which of course costs real dollars, the theory is failing to explain what is going on.
Standard competition! I do not flip out about this. But obviously there are some world views where you might view these (and other) actors as evil, and furthermore think they can act without much constraint, and so on. Those are not my world views. I see a lot of competition and a lot of ego, and then I think of Adam Smith. Standard stuff, just at much higher stakes than usual.
METR, EA, and others being attacked
Always focus on what you can learn from people and groups. Criticize in ways where you can learn something from the answer (or non-answer) and from the dialogue. If you attack how people look, their sex lives, their donors, whatever, it will make the critic stupider. It may not even hurt the target of the attack, as it provides valuable publicity and also makes them look powerful. Furthermore, there is nothing per se wrong with being “weird.” What does that really mean anyway? I’ve spent much of my life looking for “the weird,” though I would not frame it in those terms.
Wednesday assorted links
Very good news
Michael Kremer picked as World Bank chief economist (Bloomberg). Here are many many previous MR posts on Kremer, of course Alex also worked with him on Operation Warp Speed.
What Regulatory Capture Actually Looks Like
It’s amazing how a theory can take over a brain. Consider the idea that people believe what serves their interests. As heuristics go, it’s a good one. I use it all the time. Yet when Dario Amodei says AI is dangerous, perhaps even an extinction risk, some people conclude he must be running a marketing campaign. That is stupid. Which is more likely, that a useful heuristic sometimes misfires or that “our product might kill you” is a clever way to sell it? Death threats are a poor marketing strategy.
We also have plenty of evidence that the fears of AI experts are sincere. Amodei, Altman and Musk were all publicly warning about AI risk long before they had AI companies to promote. The worry runs well beyond the executive suite; rank and file researchers share it. And it extends outside the industry altogether, to computer scientists with no product to sell, among them Nobel laureate Geoffrey Hinton. Hinton left a high-paying job at Google precisely so he could speak out and Hinton is not in a Berkeley polycule with Eliezer Yudkowsky, at least as far as I know. Whatever else you may say about the belief that AI presents a serious risk, plenty of AI researchers believe it sincerely.
Similarly, when Amodei recently proposed to slow the pace and install independent safety teams at AI companies many people jumped to the conclusion that this was regulatory capture. Sorry, but no, that theory doesn’t make sense. To see why, we should review the theory of regulatory capture.
Regulatory capture came out of the political science literature especially Marver Bernstein’s 1955 classic, Regulating Business by Independent Commission. Bernstein argues for a regulatory life cycle: Gestation, Youth, Maturity, Old Age. A scandal brings a bureaucracy into existence—or gives an existing one new powers. The public’s attention, like Sauron’s eye, fixes on the issue of the day: something must be done. The thalidomide scandal, for example, helped establish the modern FDA.
Gestation gives way to a youthful burst of reform and the do-gooders come to Washington ready to battle the industry. Inevitably, however, the public’s eye looks elsewhere. But the industry never looks away. It lobbies Congress, hires former regulators, trains future ones, and supplies much of the information the agency needs. As the agency matures, accommodation replaces confrontation. By old age, the regulator has become the industry’s protector. The classic example is the ICC, created to regulate the railroads but it eventually came to shield them from competition from the trucking industry.
Notice that classic regulatory capture takes time, it’s a process of erosion rather than a battle, it happens in the shadows, in the backrooms, away from the public’s eye. As Culpepper argues in Quiet Politics and Business Power, business power goes down as political salience goes up. Regulatory capture and lobbying does a good job explaining why roasting coffee beans was defined as “domestic manufacturing”, thereby lowering Starbuck’s tax rate by 2%. It does less well at explaining big cross-industry issues the public cares about such as environmental regulation or race and gender discrimination regulation. Finally, don’t confuse capture with firms making the best of a bad situation. Philip Morris supported the 2009 Tobacco Control Act not because FDA regulation was Philip Morris’s unconstrained ideal but because it knew regulation was coming and it wanted a seat at the table to nudge the rules in its favor. That’s ordinary political bargaining—or rent-seeking—not evidence that the regulator has been captured.
Now let’s evaluate Amodei’s call for regulation in light of regulatory capture theory. AI regulation is in gestation. Public attention is fixed on the industry, and much of that attention is hostile. The big profits in AI lie in automating work, and job loss is a much more salient fear than extinction. AI politics is now loud–precisely the environment in which Culpepper predicts business power will be weakest. A mature industry can bend regulation to its purposes through revolving doors, longstanding relationships and obscure rulemaking. An industry under Sauron’s eye has much less power and faces much greater risk that politics will bend regulation to its purposes. Political actors are eager for an excuse to redistribute AI rents away from capitalists and toward favored groups (ala Peltzman).
Regulation will reduce AI profits. That doesn’t prove that every rule Amodei favors is innocent of self-interest, an absurd proposition. I suspect that Amodei’s ideal may be something like a single regulated AI monopoly—safe and reasonable profitable, like the old AT&T. But that’s not the profit maximizing outcome. If transformative AI can capture even a fraction of the enormous labor market–the world’s biggest market–laissez-faire would mean vastly greater profits. Amodei may simply prefer a smaller fortune and a safer world. That is perfectly consistent with self-interest playing a role; it is not consistent with the bastardized theory that profit maximization is the only thing that matters or that “capture” is universal.
Go ahead: argue that Amodei and other AI experts are wrong about AI risk. Ask whether his proposals favor Anthropic. But calling “our product might kill you” a clever marketing and regulatory-capture strategy isn’t sophisticated analysis. The facts don’t fit regulatory capture theory and trying to make them fit requires epistemically painful Ptolemaic epicycles. Even a dull Ockham’s razor cuts through that story to the obvious alternative: Amodei actually believes what he’s saying.
The economics of cyber risk
From Aniket Baksy and Daniele Caratelli, here is part of the abstract:
Because larger firms are more attractive targets but also invest more in protection, the model generates an inverse-U relationship between firm size and attack risk, consistent with the data. Introducing cyber risk reduces firm entry by 3.6 percent, aggregate productivity by 0.6 percent, and total output by 1.8 percent. These effects arise from general equilibrium adjustments in entry, firm size, and spillovers that are absent in typical partial-equilibrium analyses. Policy responses differ sharply: appropriately designed subsidies and minimum cybersecurity requirements can raise aggregate output, while bailouts reduce it.
Note this is not a paper about AI. But it may help us develop estimates of the future costs of AI cyberattacks. We need much more effort in this direction, and I hope this subfield rises in status rapidly.
When discussing AI policy, start with China
Whatever your ideas for regulating AI, I say start with China. Do not put China as an afterthought at the end of your proposal, mentioned in a vague wish that something good ought to happen and that maybe the future of humanity can be secured.
During the 2023 U.S.-China nuclear talks, America proposed missile-launch notifications, a nuclear crisis hotline, and processes to limit the use of outer space for military conflict. China declined all of those. Blame the U.S. if you wish (do we always keep our word?), but that is what happened.
China also broke off meaningful arms control talks. You may think it is their right to play catch-up, and to be cynical of our motives, but that is what happened.
How well did China exchange timely information about Covid, and cooperate with stopping its initial spread?
Get the picture?
If you start with China in your discussion, you will end up with sensible proposals before getting too caught up in your moods of the day. If you are reading proposals or for that matter tweets for AI regulation, and the writer does not deal with these China issues in a forthright and very specific manner, you should be very suspicious indeed.
Here is Ezra and Matt Sheehan, discussing related issues (NYT).
Callum Williams on cybersecurity prices
Share prices of cyber firms have jumped around a lot in recent weeks, leading one side or the other to claim victory. But the crucial point is that, relative to the historical norm, the market is not really pricing ANYTHING big to change. There was a much bigger move in cyber stocks in both 2020-22 (up) and 2022-23 (down) but no one read “AI x-risk” into this.
Here is the full post with graph.
Hardly the final word, and I am myself more pessimistic than those numbers indicate. But at least with this we are getting somewhere concrete and scientific rather than just scare stories. As for meta-commentary on the discourse itself, you really should be asking who are the people insisting on data here, and who are the people trying to talk you away from focusing on the data so much.
Tuesday assorted links
1. Easy to bioengineer a very dangerous virus?
2. The astrophysicists are getting antsy too. Princeton is also getting nervous. If nothing else, these are huge PR own goals. The guy is a Mill scholars, can you imagine J.S. Mill tweeting that way?
4. One Chinese view of AI risk. And from Richard Hanania.
5. Arnold Kling on Polanyi knowledge.
6. Jeremy Stern profile of Mark Zuckerberg. Great piece.
7. China’s first AI-generated TV series.
8. Introducing Free Press Excursions.
9. Redux of my earlier talk/session at St. Andrews on Effective Altruism as a philosophy.
The striking thing is how late the market moved
Here’s the S&P 500 (daily close) with the key COVID and policy events marked; the numbered key is below the chart.
Event key:
- Dec 31 – China reports the Wuhan pneumonia cluster to the WHO
- Jan 21 – First confirmed US case (Washington state)
- Jan 23 – Wuhan locked down
- Jan 30 – WHO declares a global health emergency (PHEIC)
- Feb 19 – S&P 500 all-time high, 3,386
- Feb 24 – Italy outbreak; first big US selloff
- Mar 3 – Fed emergency 50 bp cut
- Mar 11 – WHO declares pandemic; Europe travel ban; NBA suspends season
- Mar 13 – US national emergency declared
- Mar 15–16 – Fed cuts to zero and restarts QE; worst day since 1987 (−12%)
- Mar 23 – Fed announces unlimited QE; market bottom at 2,237
- Mar 27 – CARES Act signed
- Apr 2 – 6.6 million initial jobless claims in one week
- Apr 20 – WTI oil futures settle below zero
- May 8 – April jobs report: 20.5 million jobs lost, 14.7% unemployment
The striking thing is how late the market moved. Wuhan was locked down and the WHO had declared an emergency a full month before the peak. The 34% drawdown then took 23 trading days, and the bottom coincided with the Fed’s unlimited-QE announcement rather than with any turn in the epidemiological news, which was still getting worse through April.
Addendum: Mostly from a query to Claude. You may fill in the missing context.