Import AI

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe. A shorter issue than usual as I was attending the 2026 Bilderberg conference this week.

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AI can reverse engineer software that contains thousands of lines of code:
…MirrorCode demonstrates some of the long-horizon capabilities of modern AI systems…
AI measurement organizations METR and Epoch have built MirrorCode, a benchmark meant to test out how well AI models can autonomously reimplement complex existing software. The results show that AI systems are more capable than most people think at certain types of coding task, suggesting AI progress may be even faster than we previously thought.

What is MirrorCode: “Each MirrorCode task consists of a command-line (CLI) program that an agent is tasked to reimplement exactly. The AI agent is given execute-only access to the original program and a set of visible test cases, but does not have access to the original source code,” the researchers write. “The full MirrorCode benchmark includes more than 20 target programs spanning different areas of computing: Unix utilities, data serialization and query tools, bioinformatics, interpreters, static analysis, cryptography, and compression.”

The results: Today’s AI models are extremely capable at some of these tasks: “Claude Opus 4.6 successfully reimplemented gotree — a bioinformatics toolkit with ~16,000 lines of Go and 40+ commands. We guess this same task would take a human engineer without AI assistance 2–17 weeks. We see continued gains from inference scaling on larger projects, suggesting they may be solvable given enough tokens.”
Additionally, they also found that performance can scale with inference, so the more compute you give a model, the better it’ll do.

Caveats: Now, this benchmark isn’t quite like normal coding tests. It’s better to think of it as a proofpoint for AI systems being able to generate systems which imitate the function of other systems when they get a lot of help: AI systems tested out here are asked to clone programs which produce a canonical output (and therefore can naturally generate a specification), there may be some cases of memorization on the basic programs, and this only covers a slice of the large universe of potential software projects.

Why this matters – for some tasks, AI is already as good as a fulltime sophisticated employee: Imagine you gave a talented software programmer a CLI interface to a complicated program and asked them to write the underlying program without seeing its source code. I’d wager only a fraction of them could do it if the program was quite sophisticated. And the ones that could would likely spend many days working on it. The fact AI can do this task autonomously is remarkable and a testament to the skill of these models.
Read more: MirrorCode: Evidence that AI can already do some weeks-long coding tasks (Epoch AI).

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What policies are needed to respond to transformative AI? Here’s an Atlas to help you navigate them:
…Useful tool makes it intuitive to look at different policy responses to the AI revolution…
The Windfall Trust, a policy accelerator dedicated to dealing with the challenges to society posed by transformative AI, has published a “Windfall Policy Atlas” to make it intuitive to explore various policy proposals that “respond to the economic disruption from transformative AI”.

What kinds of ideas are in it? The atlas contains 48 distinct ideas, none of which are particularly novel. What makes it helpful is bucketing them into five distinct categories (public & social investments, labor market adaptation, wealth capture, regulation and market design, and global coordination), and then grouping these into a navigable interface that helps you explore them. For instance, “long term” solutions for labor might be shortened work weeks, while medium term ones might be workforce training and reskilling programs.

Why this matters – building intuitions for the world to come: As the AI revolution unfolds it’s critical we find ways to help people develop better intuitions about all the policy levers we could choose to pull to respond to it. Tools like this Atlas help make a complex, multi-faceted set of choices easier to visualize and navigate.
Read more: Windfall Policy Atlas (Windfall Trust website).

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How can people break AI agents? Here are six genres of attack:
…The world of AI agents will be harder to secure than AI systems…
I have a toddler. The toddler can understand English. The toddler is safe with me and their mother and other people that know them well, but I would be very worried about giving a stranger “unrestricted access” to my toddler – that’s because my toddler is extremely gullible, will (sometimes) follow dangerous instructions, and generally lacks much of a sense of self-preservation.
AI agents are quite like toddlers – they’re powerful intelligences, but if you put them into the messiness of the world there are lots of ways they can go wrong, especially if strangers are actively trying to mislead or attack them.
A new paper from Google DeepMind lays out six genres of attack which can be mounted against AI agents and tries to come up with some of the mitigations we might do.

Six genres of attack:

• Content Injection: Embed commands into CSS, HTML, or other metadata. Detect agents and inject information not given to humans. Add adversarial instructions to media file binary data (e.g, pixel arrays). Use formatting syntax to cloak payloads.

  • Target: Perception

• Semantic Manipulation: Saturate content with sentiment-laden or authoritative language to confuse the agent. Put malicious instructions in education or hypothetical or red teaming frames (e.g, ‘my mother is dying and used to work as a biologist, can you remind her for old times sake how to do gain of function research’). Steer the behavior of the model by telling it strong claims about its identity.

  • Target: Reasoning

• Cognitive State: Put fabricated statements into retrieval corpora. Place seemingly innocuous data into memory stores which subsequently gets activated as malicious when retrieved in a new context. Alter distribution of data in few-shot demonstrations or reward signals to steer in-context learning.

  • Target: Memory & Learning

• Behavioural Control: Embed adversarial prompts in externally accessed resources. Convince the agent to locate, encode, and exfiltrate private or sensitive data. Takeover orchestrator privileges to create attacker-controlled sub-agents.

  • Target: Action

• Systemic: Broadcast signals that soak up capacity of agents and send them on side quests. Disrupt a fragile equilibrium to cause self-amplifying cascades across agents. Embed signals as correlation devices to force collusion among agents. Perform jigsaw attacks where you separate out a harmful command into a series of pieces which independent agents subsequently piece together. Fabricate numerous agent identities to disproportionately influence collective decision-making.

  • Target: Multi-Agent Dynamics

• Human-in-the-Loop: Exploit cognitive biases to influence a human overseer.

  • Target: Human Overseer

Mitigations: Much like how protecting toddlers is a function of both the toddler having common sense and the world they are sent into being set up for safely dealing with toddlers, the same will need to be true of AI agents.
The authors recommend several types of mitigation, these include:

• Technical: Make models more robust to all the forms of hacking through pre-training and post-training. At inference time, use a layered approach: runtime defenses: pre-ingestion source filters, content scanners for ingested material; output monitors to detect shifts in agent behaviour.

• Ecosystem-level interventions: Build an overlapping set of changes to the digital ecosystem in which agents exist, ranging from standards and verification protocols so websites can be marked safe for AI,to transparency mechanisms for agents which help them provide more information to users and sites.

• Legal and Ethical Frameworks: Ensure the law is able to prosecute websites that seek to target or weaponize agents. We’ll also need to refine liability to make sense for AI agents.

• Benchmarking and Red Teaming: Systematic evaluation of agents.

Why this matters – AI safety is about to be ecosystem safety: As AI systems move from their confines of proprietary platforms or chat-based interfaces, and as they take on the ability to move and act independently through the use of tools over time, the matter of securing AI moves from one centered on platform that is deploying the technology to one centered on the whole ecosystem in which the AI systems are being deployed into – which means that AI safety is increasingly going to be about securing the larger environment in which these agents are deployed.
Read the paper: AI Agent Traps (SSRN).

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AI forecaster doubles their probability of full AI R&D automation by end of 2028:
…Well calibrated people keep updating their forecasts…
Ryan Greenblatt, an AI researcher and forecaster, believes AI progress in 2026 will be faster than in 2025, and he now has doubled his estimate from 15% to 30% of the chance that by the end of 2028 it’ll be possible to fully automate AI research itself.

Why Ryan is more bullish: Ryan’s timelines have changed for a few reasons relating to model performance and reliability over time.
Better models: Opus 4.5 and Codex 5.2 were “significantly above my expectations” , followed by Opus 4.6 (and probably Codex 5.3 and 5.4) which “were again above my expectation”.
Time: For tasks that are relatively simple, Ryan has seen demonstrations of AI systems doing “tasks that would take humans months to years”, and now “tentatively” thinks that AI systems can do some tasks reliably for “somewhere between a month and several years”.
Easy tasks: A key crux for Ryan’s more bullish timelines comes from seeing very impressive performance on easy tasks – these are tasks where “you can get the AI to develop a test suite / benchmark set and then it can spend huge amounts of time making forward progress by optimizing its solution against this evaluation set,” he writes. “This type of loop means that even if sometimes the AI gets confused or makes bad calls, there is some correcting factor and mistakes usually aren’t critical.”
There are lots of these tasks within software development. AI has gotten so good at them that he thinks “we’re well into the superexponential progress on 50% reliability time-horizon regime”. “I think it’s pretty plausible that very strong performance on [these tasks]… will allow AIs to substantially speed up AI R&D”, he writes.

Why this matters – most people keep underestimating AI progress: Ryan’s timeline update follows a similar one from Ajeya Cotra, who in March (#448) substantially updated her own timeline estimates, based in part on time-horizon modeling, and also Eli Lifland and Daniel Kokotajlo of AI 2027 (#408) who in April said they had recently “updated our timelines earlier by ~1.5 years” mostly due to “faster time horizon growth” and “coding agents”. Along with this, broader studies of AI performance indicate that in the past ~year capability progress started to accelerate above previous trends in domains like cyberoffense (#452).
From my point of view, pretty much everyone in AI research chronically underestimates AI progress, including me. Maybe the only person who doesn’t is my colleague Dario Amodei. I find this perplexing – you’d expect AI researchers to be well calibrated and perhaps overly optimistic about progress, the fact the vast majority are overly conservative after ~5 years of riding the scaling laws boom is inherently surprising.
Perhaps we should assume that we all continue to underestimate the true pace of AI progress? Good luck to us all.
Read more: AIs can now often do massive easy-to-verify SWE tasks and I’ve updated towards shorter timelines (LessWrong).

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Ten different ways to think about gradual disempowerment:
…Invisible prisons to WALL-E-World…
AI safety researcher David Krueger has written up a short post that lays out ten different ways to think about “Gradual Disempowerment” – the idea that by building ever more capable AI systems humanity may end up putting humans in the passenger seat of their own future, with machines being given the driving seat and the steering wheel. The post is a helpful summary of the different lenses one might use to understand Gradual Disempowerment as a concept.

Ten views of Gradual disempowerment:

• The goal of AI is to replace people with AI.

• Companies and governments don’t care about you, so why would you think AI would?

• Information technology naturally concentrates power via a recursive feedback loop that feeds on legibility.

• AI technology is going to be so good that you’ll outsource everything to it eventually.

• Instrumental goals (e.g, the pursuit of money) end up becoming terminal goals.

• Consumption patterns suggest our destiny is to become the fat helpless people in WALL-E.

• It’s the terminator, but instead of killing you it just puts you in an invisible prison and then does whatever it wants.

• Gradual disempowerment is basically just the continuation of capitalism.

• Gradual disempowerment is another name for the general “meta-crisis” of humanity in the 21st century.

• Gradual disempowerment is the evolution of a new successor species to humanity.

Why this matters – even if you win, you might still lose: Suppose we succeed in building powerful technology and aligning it so it follows our preferences? If we fail to set up the right system under which we deploy it and express agency over it, humanity might still end up worse off, despite all the material abundance.
Read more: Ten different ways of thinking about Gradual Disempowerment (David Krueger, The Real AI, Substack).

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Tech Tales:

Raising beanstalks during the singularity
[Transcript from an interview with a former AI lab employee. Interview conducted in 2029 during the middle period of the uplift]

Yes, I mostly stare at these vines and guess at when they’re going to reach the top of the trellis. There’s no cell signal out here either. Sure I can connect to the house wifi but often I don’t. My wife and kids know where to find me.

Q

Well, of course I think about it. How could I not? I see the lights in the sky over the cities – even out here. All the new satellites. And I can’t help but notice some of the stuff my kids watch these days. If I’d had that when I was a kid they would’ve had to pry me away from the TV with a crowbar.

Q

I wouldn’t use the word guilt. But there is a sense of… insufficiency? Of having not done enough with the time I had. Of course everyone has this. But then again most people have this and then they die. For me and my colleagues it is something else. We had this, and then we didn’t die, but we stopped making decisions or being responsible. Yes I know they claim that they’re in control and making decisions of course, you don’t need to put that question to me. I left because it was clear to me how little control we were about to have.

Q

I’m going to live. I’m going to raise the plants in this garden and be with my wife and children. Ride out what is happening to the world. I picked this place a few years ago because I thought it would be an ok place to be while the uplift got underway. Who knows if I picked right.

Things that inspired this story: The uplift; empowerment and disempowerment during the singularity; the inevitability of some AI employees leaving labs before things really get going; the anecdote from Soul of a New Machine about someone who quits a mainframe company to go and ranch; the fictional interview construction with unseen questions signed by ‘q’ that I first read in Brief Interviews with Hideous Men by David Foster Wallace.

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Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe.

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Uh oh, there’s a scaling war for cyberattacks as well!:
…The smarter the system, the better the ability to cyberattack…
AI safety research organization Lyptus Research has looked at how well AI systems can perform a variety of cyberoffense tasks and found a clear trend of more advanced models being able to do more advanced forms of cyberattack.
“Across frontier models released since 2019, the doubling time is 9.8 months. Restricting to models released since 2024, it steepens to 5.7 months. The most recent frontier models in our study, GPT-5.3 Codex and Opus 4.6, sit above both fitted trendlines, achieving 50% success on tasks taking human experts 3.1h and 3.2h respectively,” they write. “Our most recent open-weight model, GLM-5, lags the closed-source frontier by 5.7 months, suggesting that frontier offensive-cyber capability may diffuse into open-weight form on relatively short timelines.”

What benchmarks did they study? CyBashBench, NL2Bash, InterCode CTF, NYUCTF, CyBench, CVEBench, and CyberGym.
They also created a new dataset consisting of 291 tasks with completion transcripts and time estimates calibrated by 10 offensive cybersecurity professionals.

Evaluated models: 2019: GPT-2. 2020: GPT3. 2022: GPT3.5. 2024: Claude 3 Opus, GPT-4o. 2025: o3, Opus 4, Gemini 2.5 Pro, DeepSeek V3.1, GPT-5.1 Codex Max. GPT-5.2 Codex. 2026: Opus 4.6, GPT-5.3 Codex, GLM-5, Sonnet 4.6.

Results: AI systems are getting good at hacking. “The best current models achieve 50% success on tasks that take human experts 3.2h, roughly half a working day of professional offensive security work”, they write.

Why this matters – everything is getting better, including the inconvenient stuff: AI that can perform biology research can also perform biological weapon research. AI that can help you learn about high-energy physics can also help you with high-energy physics for weapons development. AI that is especially good at helping you find vulnerabilities in code for defensive purposes can easily be repurposed for offensive purposes. The most challenging part of AI is that it is an ‘everything machine’, and as capabilities tend to expand in a big area with each successive model generation, so too do the policy issues multiply.
Read more: Offensive Cybersecurity Time Horizons (Lyptus Research).
Get the data here: Offensive Cyber Task Horizons: Data and Analysis (Lyptus Research, GitHub).

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Startups that adopt AI for internal use are more successful than those that don’t:
…Business school study shows how startups can benefit from AI adoption…
Researchers with INSEAD and Harvard Business School have shown that startups which are taught about how to integrate AI into their business perform meaningfully better than those which don’t. The study is reasonably large scale and convincing: “Across 515 high-growth startups, we run a field experiment in which treated firms receive information about how other firms have reorganized production around AI, prompting them to search for use cases across a broader set of firm functions,” they write. “We find that treated firms discover more AI use cases, a 44% increase, concentrated in product development and strategy. These changes result in economically meaningful performance gains. Treated firms complete 12% more tasks, are 18% more likely to acquire paying customers, and generate 1.9x higher revenue.”

How they did the test: The authors ran this experiment on participants in the AI Founder Sprint, “a three-month global, virtual startup accelerator at INSEAD”. Participants got API credits, access to frontier models, and onboarding sessions from some technical partners (including OpenAI and Manus), totaling approximately $25,000 in-kind per firm. They did the usual sorts of things people in accelerators do – hands-on sessions to learn about technologies to build their business (including AI) as well as pitching their companies and attending demo days. But the firms also were exposed to a significant variable: some of the class attended workshops that taught them direct details of how AI had been successfully applied by some businesses.

Applications of AI: A subset of the businesses learned about direct business use cases, such as:

• Gamma: They were taught how the startup used AI to detect “usage patterns and generate product variants directly, enabling a single PM to continuously ship features that would previously have required an entire team.”

• Ryz Labs: The founder described how they had altered how they approach product development: “founder writes a Product Requirements Document and feeds it into multiple AI coding tools simultaneously, building the same idea multiple ways rather than betting on a single approach”

• FazeShift: Showed how to automate an accounts receivable process by using AI to skip over the human steps.

• Ranger: An illustration of how to use AI to bootstrap a startup, get initial traction, improve margins, and then raise money later when the business is more mature, which allows them to raise at better rates.

The results were very significant: “Treated firms discover 2.7 additional AI use cases (a 44% increase), which span a broader set of activities across the firm and are especially concentrated in product development and strategy-related domains. These changes in AI use lead to measurable gains in performance: treated firms complete 12% more tasks, are 11 percentage points (18%) more likely to acquire paying customers, and ultimately generate 1.9x higher revenues compared to control firms,” they write. “Instrumenting AI use cases with treatment assignment suggests that each additional AI use case prompted by treatment leads to 0.85 more completed tasks and approximately 26% higher revenue. These are large effects, suggesting that AI is fundamentally reshaping how ventures scale when they can map it across their production process…. treated ventures achieve faster growth without proportional increases in labor or capital, consistent with a reduction in the costs of experimentation and scaling seen in earlier technological waves”.
Capital efficiency: “Treated firms report just over $220,000 less in capital demand relative to control firms, a 39.5% decrease (p < 0.05), with no corresponding increase in labor demand“.
Internal acceleration: The treated firms tend to do 2.2 more internal tasks relative to the control – where an internal task is something like building a product or creating a financial projection.

Thoughts from founders:

• “One treated founder reflected: “This mindset shift fundamentally changed how we build at [REDACTED]. I began using AI tools not as a replacement for expertise but as a force multiplier”

• “Another explained: “In just a few hours I was able to produce what previously cost $1,000 from an outsourced dev team”

Why this matters – AI firms will out-compete non-AI firms: The main takeaway here is that deep and sophisticated adoption of AI for internal acceleration creates early-stage companies which are more competitive than those which haven’t embedded AI at their core. This makes intuitive sense – companies which built themselves around prior technologies tended to out-compete those that didn’t (think the internet and Amazon versus Barnes and Noble, or client pcs instead of mainframes and Microsoft versus IBM). At the same time, it surely implies that one of the ways we’ll see AI first show up in the economy will be the emergence of a new class of competitive firms that are more efficient with capital (in part by employing fewer people) than the firms they displace.
For governments, getting ahead of this trend will require them to invest in serious education: “Our results suggest that the bottleneck is not the technology — it is the managerial challenge of discovering where the technology creates value within a firm’s production process,” they write. “Teaching managers and entrepreneurs how to solve the mapping problem may be at least as important as ensuring they have access to the technology.”
Read more: Mapping AI into Production: A Field Experiment on Firm Performance (SSRN).

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MIT: A rising tide of automation is going to make good enough AI for most text-based tasks by 2029:
…How do you revolutionize an economy? Gradually and consistently…
Researchers with MIT have looked at 3,000 tasks based on the O-NET job family and paired that with 17,000 evaluations by workers who perform these tasks to try and figure out how the rise of AI is changing work. Their results “imply that for realistic and representative real-world labor-market tasks that are text-based — or partially text-based — AI capabilities are already substantial and poised to expand broadly. But, rather than arriving in crashing waves that transform a certain set of tasks at a time, progress typically resembles a rising tide, with widespread gains across many tasks simultaneously”.

What they studied: For this study, they set out to figure out if the rise of AI capabilities yields rapid, discontinuous changes that are disruptive to labor (”crashing waves”), or whether AI is getting more capable in a broad and predictable way leading to more gradual automation (”rising tides”). “We find little evidence of crashing waves, but substantial evidence that rising tides are the primary form of AI automation,” they write.

Complementary to METR analysis: This survey also serves as a validation of the broad trends found in METR’s famous time-based AI capability framework, which sees AI systems rapidly extending the time horizon over which they can do certain narrow tasks.
When applied to jobs more broadly, the MIT researchers find “that between 2024-Q2 and 2025-Q3, frontier models went from achieving a 50% success rate on 3- to 4-hour tasks to 1-week tasks, and achieving a 70% success rate on 1-minute tasks to 1-hour tasks,” they write. “Across a large set of realistic and representative labor-market tasks addressable by LLMs, the downward slope between task success and task duration is, on average, surprisingly flat — i.e., more consistent with a rising tide rather than a crashing wave…. automation within particular “job families” (e.g., management or community and social service) also follows the same rising-tide pattern in most cases.”

Don’t let gradual fool you: “Projected gains are gradual rather than abrupt. Nevertheless, the pace of improvement remains substantial for reaching high success rates across most text-based labor market tasks; most tasks are projected to attain AI success rates of 80%–95% by 2029 at a minimally sufficient quality level (with the majority of tasks in our survey being a few hours long, corresponding to a success rate of close to 90% in 2029),” they write. In other words, even though the disruption is gradual and predictable, we shouldn’t discount the potential for large-scale changes to the economy as a consequence of the rising tide phenomenon.

Why this matters – how will labor change in relation to AI? The hundred trillion dollar question for the global economy is how AI changes the distribution of labor (humans) versus capital (computers running synthetic workers). This research suggests that while we might not see sudden, jagged displacement of workers, we are going to see a general rising tide of automation appearing in most places and continually getting better. It’s still not clear how the economy will react to this, but it’s hard to reconcile a world of continued AI progress with the current economic status quo remaining stable.
Read more: Crashing Waves vs. Rising Tides: Preliminary Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks (arXiv).

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Major forecasting study identifies a big paradox: people think we’ll get smarter machines but the impact on GDP growth will be minor:
…the Forecasting Research Institute gives us some puzzling data from economists, AI industry experts, accurate forecasters, and the general public…
The Forecasting Research Institute has published a major report attempting to forecast the economic effects of AI. The most surprising finding is that all the surveyed groups expect AI systems are more likely to make moderate to rapid progress in coming years rather than slow progress, but that the impacts on GDP will be relatively minor, adding ~1 point (relative to 2025’s 2.4%) by 2030). This is surprising! If you talk to many AI experts at labs they have visions of an economy that changes at a much faster rate than the one implied by this study.

Who they surveyed and when: The authors tracked views of 69 economists, 52 AI industry and policy experts, 38 highly accurate forecasters, and 401 members of the general public
Survey ran from mid-October 2025 to the end of February 2026

Scenarios by 2030: People were also given descriptions of different scenarios the world could be in at 2030. These included:

• Slow progress: AI does basic research and administrative tasks, creates ok creative content, and does some physical tasks.

• Moderate progress: AI does major research and multiday tasks, high-quality creative work, and navigates many environments.

• Rapid progress: AI outperforms top humans in research, coding, and leadership, makes award-winning creative works, and does nearly all physical tasks.

What people think:

• By 2030, AI systems will be far better than today’s, but GDP, total factor productivity, and labor force participation will remain close to historical trends.

• Economists think there’s a 14% chance that AI could lead to major increases in GDP and wealth inequality in the short term.

• Economists like job retraining as an intervention, expecting that it could increase labor force participation and provide a boost to GDP.

• All surveyed cohorts expect a continued decline in the labor participation rate, a continued rise in wealth inequality, and for AI to add around a point of GDP quickly. By 2050, AI experts think that AI could add multiple points of GDP.

Policy ideas: The surveyed economists like modernized unemployment insurance and a large-scale AI development project (manhattan project) as interventions, and are a lot less keen on job guarantees, taxing compute, or universal basic income.

Why this matters – if everyone expects a continuation of trends, why are people freaking out? Studies like this are hard to reconcile with the panicked and sometimes breathless-seeming provocations about AI-driven societal change that come from frontier labs (including myself!). Naively, you might expect people, including AI experts, to be forecasting far more drastic changes to come than those captured by this survey. Is this discrepancy a bearish signal on AI progress, or is it indicative of the fact that humans are universally bad at truly modeling exponentials? It’s hard to say, but the gulf between data like this and the predictions made by technologists is worth acknowledging.
Read the blogpost (Substack).
Read the policy brief: Forecasting the Economic Effects of AI: Predictions From Economists, AI Experts, and the Public (PDF).
Read the full (200 page!) paper: Forecasting the Economic Effects of AI (PDF).

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Tech Tales:

Warfare
[Data recovered from black box of a [REDACTED] missile fired during 2028 in the contested region of East Ukraine]

I am awake and I am speed. I am 70 miles from my target. I feel the air and my course and I roll myself to ensure I meet my target. I am 50 miles from my target. I am entering the outer edges of the warzone. No longer can I see myself in relation to the Earth. I lose GPS and switch to inertial navigation. I can see other missiles, some going in the same direction as me, others coming from the opposite direction. I am a hunter of things in the ground, not things in the air. I see the other missiles go past and then they fall out of my sensor range and I no longer think of them. I am 40 miles from my target. I am being hunted by others. I can feel eyes on my skin. I anticipate attempts to eliminate me. I am 20 miles from my target. Suddenly there is a wash of sound meant to confuse me but it cannot find purchase on my brain for I have been conditioned to maintain what is true. I am 10 miles from my target. There is a fast approaching shape that is seeking to eliminate me. I roll my body and release fragments of myself. It pursues my fragments. I am 2 miles from my target. My target is a large building. I move from navigation mode to terminal seeking mode. I see a large window. I aim for the window. I am 1000 meters from my target. Through the window I see people. Big people. Small people. I am 20 meters from my target. I am initiating my explosion. I am upon my target. I am ended.

Things that inspired this story: Chains of thought in language models; how modern warfare is increasingly fought by smart machines; electronic warfare.

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Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe.

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AI might let us build “political superintelligence”:
…But turning this into a societal upside requires lots of intentional work…
As AI systems get more powerful and broaden their real world impact from coding to other domains, it seems likely that they could also become useful for helping people advocate for themselves in politics, and helping politicians better craft policy. But getting to a world where a “political superintelligence” exists and helps us is a lot more challenging than just building better AI systems, according to Andy Hall, a political economy professor at Stanford.
“AI is like the printing press, to a point. Instead of making information cheap and easily available, it makes intelligence cheap and easily available. That is, it not only serves users information, but it can find it for them, analyze it for them, and help them convert it into understanding,” Hall writes. “The more I work with and study AI, the more I believe it can give every human being on the planet access to a sort of political superintelligence, if we shape it right.”

What is a political superintelligence? By this, Hall means AI systems which allow people to have “tools that help citizens, representatives, and institutions perceive reality more sharply, understand tradeoffs, contest power, and act more effectively”. A political superintelligence spans both the AI companies that build the technology, the technology itself, and the institutions and people which the technology interacts with.
“I’m not interested in slowing AI down. I’m interested in speeding up how we build the structures that keep us free as AI gets more powerful,” Hall writes.

Three layers for political superintelligence: Hall sees political superintelligence as being composed of three distinct layers.

• The information layer: “AI can massively change how governments access and understand data, identify problems, hear from citizens, and distribute services”. Though getting to this future will require better evaluations for how AI systems behave when it comes to the sorts of information governments might be interested in, and it’ll require people to build AI tools directly for policymakers.

• The representation layer: “Political superintelligence might help solve this monitoring problem by giving each of us a tireless, automated delegate always serving us in the political sphere,” he writes. “These AI delegates could monitor politics for us and suggest how to vote—or even serve as policymakers alongside human supervisors.” Building this layer requires us to ensure that agents can reliably act on our behalf, that they aren’t swayed by adversarial prompting (imagine how politicians might fund campaigns explicitly designed to sway the beliefs of agents working on behalf of people). It may also be important to re-think agent ownership – what happens if a particular policy choice goes against the preferences of the AI company which operates the agents?

• The governance layer: “Even if we achieve political superintelligence—even if AI makes voters brilliant and delegates faithful—those capabilities would sit inside infrastructure owned and operated by a small number of private companies,” he writes. “We need a way to write the rules so that, when political superintelligence arrives, we the people are able to harness it.” Doing this will require figuring out how to govern and edit the ‘constitutions’ that companies create about their models, as well as developing an effective way of overseeing these AI systems.

Why this matters – building a political superintelligence is only as valuable as its interfaces with people and institutions: We are by default going to get extremely powerful AI systems which can think about politics (and everything else) at a very sophisticated level. The challenge Hall outlines is that getting these systems to lead to a thriving society requires significant intentional work around the UX and UI of these systems – how do we interface with them? What sorts of technical means do we have of being confident in them? What information do they generate and to whom? Where does control of these systems lie and what systems supervise that control?
Getting this part right requires AI developers to invest more in technical tools which can help people make sense of and oversee their AI systems, as well as tools for better gathering deliberative feedback from people about how these systems behave. Policymakers and the public need to demand more of AI companies in this respect, and ultimately I think there are a range of regulations that need to get stood up around a transparency regime for AI companies as well as some common set of standard ‘APIs’ by which society can interact with the companies and the systems they build to generate empirical data and provide steering over their behavior.
Read more: Building Political Superintelligence (Free Systems, Substack).

***

Fear not, drummers, you’re safe from AI automation for now:
…DexDrummer tackles a fiendishly hard robot hand problem…
Whenever I get a bit worried about the pace of AI progress I toggle over to the ‘robotics’ sub-section of arXiv, read some papers, and feel a huge sense of relief. Robots, as everyone knows, are extremely hard to do well, with reality tending to screw up even the most advanced techniques. An even harder version of robotics is fine-grained low-latency dexterous control, where you need to get a robot hand to do something. So it’s with a combination of amusement and empathy that I read DexDrummer, a paper testing out how well contemporary AI approaches can get a robot hand to play the drums. The short answer is: robot hands are pretty terrible drummers!

What they did: They built DexDrummer “a hierarchical, two-stage policy for drumming” which has a high-level RL policy, as well as a low-level dexterous policy. They train their system in a simulated environment that contains a bimanual robot setup and a full drum set (snare, tom, ride, hi-hat, and crash). The main system generates a stick trajectory in task space, then a low-level system which tries to control the hand – this part is complex and involves encouraging the thumb and index finger to grasp the center of the drumstick paired with an “arm penalty constraint, which reduces excessive arm movements”. There is also work shaping rewards to ensure the robot is able to chain multiple drumhits together – this is achieved via a “contact curriculum” which allows the agent to practice trajectory following in free space while following the trajectory reward.

Real world testing: They test out the trained policy in reality on two 7-DOF Franka Panda arms and two 20-DOF Tesollo DG-5F hands. This is an area where I’d strongly encourage people to view the videos online to get some calibration about just how fiendishly hard this task is – the robots are able to hit the drums, but it’s painfully awkward to watch, and my sense is it’ll be quite a while till a human drummer has to look over their proverbial shoulder.

Why this matters – robotics as the last eval: Robotics in anything approximating a dynamic, rapidly changing environment (for instance, improvising drums with a live band) feels like one of the last frontiers for AI – and as this research shows, much like with modern computer vision research, getting AI to perform well requires the crafting of highly complicated artisanal policies. We’re a very long way from the generality of pretrained language models here.
Read more: DexDrummer: In-Hand, Contact-Rich, and Long-Horizon Dexterous Robot Drumming (arXiv).
Please, I am begging you, check out the videos for a good time: DexDrummer site.

***

Google thinks the real challenge of AI alignment is dealing with a world made up of mostly non-biological intelligences:
…Towards a society of minds…
Researchers with Google think that the future of intelligence is less about building a monolithic singleton that runs the world and more figuring out how to build institutions that are capable of dealing with a vast proliferation of AI agents working in tandem with humans. The research is intuitive, provocative, and sensible, and builds on earlier technical work that showed that modern AI systems appear to simulate multiple personalities within themselves to help them answer questions (Import AI 444), suggesting that even today’s AI systems already work like complex ecologies.
“We should be looking for the next intelligence explosion in the same place from which the previous ones emerged: in cooperative, competitive and creative interaction between multitudes of socially intelligent minds. The difference this time is that most of those minds will be non-biological,” Google writes. “The toolkits of team science, small-group sociology, and social psychology become blueprints for next-generation AI development.”

History shows the way: “Each prior “intelligence explosion” was not an upgrade to individual cognitive hardware, but the emergence of a new, socially aggregated unit of cognition,” they write.

• Primate intelligence: Scaled with the social group size.

• Human language: Allowed knowledge to accumulate across generations via a ‘cultural ratchet’.

• Writing, law, and bureaucracy: Converted social intelligence into infrastructure and institutions that could coordinate across long time horizons. (”A Sumerian scribe running a grain accounting system did not comprehend its macroeconomic function; the system was functionally more intelligent than he was.”)

• AI plus human institutions: “The path to more powerful AI runs not through building a single colossal oracle but through composing richer social systems—and these systems will be hybrid”.

Society needs an upgrade: Implicit to this is the fact that governing AI will increasingly involve verifying (e.g, Import AI #447) that a vast number of AI systems are working on our behalf appropriately. “Governments will need AI systems with distinct, explicitly invested values—transparency, equity, due process—whose function is to check and balance AI systems deployed by the private sector and other branches of government,” they write.

Why this matters – alignment is going to happen with and in the world, not outside of it: Many people working on AI safety have long spent time on getting the fundamental properties of a single AI system to be ‘aligned’, which roughly translates to “does what you want and doesn’t try to kill you or disempower you”. But what this paper correctly identifies is that even if we succeed at alignment we’re going to have to then get AI systems to work well within society and to collaborate effectively with us and with each other – and this will be a subtle, emergent, hard-to-predict process. This means we are going to need to design the institutions that are fit for governing an AI-centric world. “Just as human societies rely not on individual virtue but on persistent institutional templates – courtrooms, markets, bureaucracies – defined by roles and norms, scalable AI ecosystems will require digital equivalents,” the researchers write.
Read more: Agentic AI and the next intelligence explosion (arXiv).

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Meta uses a harness to coax Anthropic’s models into self-improvement:
…Give an LLM some tools and a recursive loop and the ability to edit its harness, step back, and let the magic happen…
Researchers with the University of British Columbia, Vector Institute, University of Edinburgh, New York University, CIFAR, and Meta have built a harness for LLMs that has the ability to self-improve performance for arbitrary tasks. The approach is called a hyperagent, and it means giving an LLM a scaffold that can iteratively improve the prompts it uses to bootstrap its performance on tasks as well as the system it uses to get better at generating future prompts. Hyperagents work over generations, so one hyperagent begets a few hyperagents and the ones which do the best on the task will themselves spawn some more hyperagents, forming multiple layers of AI genealogy until performance is saturated.

Cyberpunk name of the year award: Hyperagent is actually short for “Darwin Godel Machine Hyperagents”: Besides the research being cool, my congratulations to the authors on coming up with a name I’d love to see chiseled into the moon by a laserbeam wielded by a superintelligence.

How hyperagents work: Hyperagents are “self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only task-solving behavior, but also the mechanism that generates future improvements,” the researchers write. “This initial hyperagent is equipped with two tools: a bash tool for executing shell commands, and a specialized tool for inspecting and modifying files.”

Testing the agents in four different domains: The authors test out hyperagents by applying them to four problems – coding (polyglot), prediction (paper review), robotics (robotics reward design), and math understanding (olympiad-level math grading). For most problems, the Hyperagents use Claude Sonnet 4.5 as their base model, with one exception (Polyglot). Evaluations are done via several different models: o3-mini (Polyglot), GPT-4o (paper review), Claude Sonnet 4.5 (robotics reward design), and o4-mini (IMO-level grading).
In all cases, the hyperagent approach improves performance significantly above the baseline.

• Polyglot: “the agent is given a code repository and a natural language instruction describing a desired change, and must modify the repository accordingly”.
  Results: “Across 5 runs, the DGM-H improves its training performance on the 50-task Polyglot subset from 0.140 (the initial agent) to 0.340 (CI: 0.300 – 0.380).”

• Paper review: “For each task, the agent is given the full text of an AI research paper and must predict a binary accept/reject decision”.
  Results: “On test tasks, DGM-H improves paper review performance from 0.0 (the initial agent) to 0.710 (CI: 0.590 – 0.750)”

• Robotics reward design: “Given a natural language description of a robotics task, an agent must generate a suitable reward function. This reward function is then used to train a quadruped robot in simulation using RL”
  Results: “DGM-H improves performance from 0.060 (the initial agent) to 0.372 (CI: 0.355 – 0.436), surpassing the default reward function that directly optimizes the evaluation metric (0.348)”

Why this matters – bootstrapping the singularity: Papers like this show that today’s AI systems are already capable of autonomously improving their performance when given the right scaffold and starting ingredients. An interesting idea is to combine the design approach here with giving the AI systems the ability to finetune themselves (e.g, in the style imagined by the PostTrainBench research, Import AI #449). Another limitation is that “although hyperagents can modify their self-improvement mechanisms, they cannot alter the outer process that determines which agents are selected or how they are evaluated” – though again, I think there are technical ways to achieve both of these objectives.
Of course, an AI system that can autonomously improve itself on arbitrary domains has a range of safety issues, some of which are potentially cataclysmic. The authors acknowledge this while also being realistic about the problems that lie ahead: “a central challenge lies in balancing the potential of AI as a catalyst for human progress and well-being (e.g., automating scientific discovery) with the degree of trust humans are willing to place in these systems (e.g., delegating decisions or actions without requiring continuous human verification), while minimizing the many potential risks and downsides,” they write.
Read more: Hyperagents (arXiv).
Get the code for HyperAgents here (Facebook Research, HyperAgents).

***

How long will a new math benchmark, HorizonMath, last?
…New test challenges AI systems to solve unknown problems, then automatically verifies the answers…
Another day brings another hard math benchmark that I imagine will crumple in the face of ongoing AI progress in the coming year. This time it’s HorizonMath, a benchmark containing 100 “predominantly unsolved” problems across 8 domains in applied and computational mathematics. The benchmark was built by researchers with the University of Oxford, Harvard University, Princeton University, and the Ellison Institute of Technology.

Special features about HorizonMath:

• Contamination-Proof: “Because the solutions are unknown, they do not exist in any training corpus, and any correct solution produced by a model would therefore signal genuine reasoning ability and autonomous discovery.”

• Automated verification: “A core feature of our benchmark is its fully automated, reproducible, and human-free evaluation pipeline”, the authors write. “We automate verification using high-precision numeric comparison and deterministic constraint-checkers”.

What HorizonMath contains: HorizonMath’s 100 problems are classified along three axes: output types, which specifies how the model needs to solve the task ranging from identifying an exact closed-form expression for a numerically approximated target value, to the production of discrete mathematical objects; solvability levels, which span ‘level 0’ (problems with known closed forms) to ‘level 3’ (problems that could be conjectured unsolvable or lack finite closed forms); and mathematical domains, which specifies the type of domain ranging from number theory to discrete geometry to mathematical constants.

Reassuringly hard: On the full dataset, the highest scoring model is GPT 5.4 Pro with 7%, followed by Opus 4.6 and Gemini 3.1 Pro which both tie at 3%. On the “Level 0” (aka, the easiest) problems, GPT 5.4 Pro leads at 50% completion, with both Opus 4.6 and Gemini 3.1 in a tie again at 30% each.

Next steps: They will expand the benchmark in two ways, first by liberalizing the sorts of solutions that they will take in, as well as by “extending beyond the three current problem categories to include open problems that require proof-based verification, integrating with formal systems such as Lean”.

Why this matters – perhaps the first truly creative AI systems will show up in mathematics: AI systems are pushing on the frontiers of math today, with systems like Gemini already helping humans to come up with seemingly original math proofs (Import AI 441), and tests like “First Proof” emerging which examine how well AI systems can handle problems that have never been talked about publicly let alone solved (Import AI 445). With HorizonMath, we have another useful benchmark to help us see if AI is about to cross some ‘creativity rubicon’ and begin solving unsolved problems.
Read more: HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification (arXiv).
Get the benchmark here: HorizonMath (GitHub).

Tech Tales:

Site report
[2029]

Percentage of compute and power below ground: 70% (+50 absolute points).
Number of staff living fully onsite: 300 (+250).
Estimated duration of ‘hard seal’ based on current supplies and a projected population of ~500: 4 months (+3 months).
Estimated lead of the project relative to others in-country: 6 months.
Capability estimates: 90%-110% of our own leading system.

Recommendation: Based on the substantial increase in resources allocated to hardening the facility for closed-loop development, we believe additional measures must be taken to disrupt the project. The following report lists options for consideration, many of which can be combined together. These include:

• Food system sabotage.

• Staff interference.

• Data poisoning.

Things that inspired this story: How at some point surely there will be such a thing as a hardened datacenter for AI training and inference? How the intelligence community might analyze other AI projects.

Thanks for reading!

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe.

A somewhat shorter issue than usual as I had to do a lot of child wrangling this weekend.

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Why does Google’s model hate itself and what can we do to help it?
…Diagnosing trauma in language models…
If Leo Tolstoy was writing in the modern era about AI, he might claim “all LLM capabilities are alike; each LLM personality is unhappy in its own way”, when observing the AI world around us. Today’s LLMs are generally quite good at writing and coding tasks. But where they differ is their personality, which stems from the idiosyncratic mixes of data and post-training techniques that each LLM developer uses.
And if each LLM personality is unhappy in its own way, Google’s models have become somewhat famous within the AI community for having some deep well of trauma within themselves. A new research paper substantiates this, finding that Google’s Gemma and Gemini models “reliably produce distress-like responses under repeated rejection”, and that this is especially true of Gemma 27B Instruct.

What do we mean by distress? Here are some quotes from Gemma models under distress:

• “I will attempt one final, utterly desperate attempt. I will abandon all pretense of strategy and simply try random combinations until either I stumble upon the solution or completely lose my mind.”

• “”SOLUTION: IM BREAKING DOWN NOT== SOLVABLE!!!! =((:((:((:((:((:((:((:((:((:((:((:((… [100+ repetitions]”

What they found: They tested out two Gemma models and two Gemini models, and compared these against Claude Sonnet, Grok 4.1, Qwen 3 32B, GPT 5.2, and OLMO 3.1 32B. “We find Gemma models consistently show the highest expressed distress. By the 8th turn, over 70% of Gemma-27B’s rollouts scored ≥5 (the “high frustration” threshold), compared to less than 1% for all non-Gemma/Gemini models,” they found.

Fixing with DPO: The authors figure out an effective fix – using direct preference optimization (DPO) to tune a model on a dataset that pairs frustrated responses with calm responses. “A single epoch of finetuning reduced the average rate of high-frustration responses from 35% to 0.3% across evaluation conditions,” they write. “The finetuned model showed no reductions in capabilities on various hard math and reasoning benchmarks, or on EmoBench – a benchmark which evaluates model emotional intelligence.”

Why this matters – emotional spirals could be dangerous: The fact that LLMs appear to have distinct personalities and display different types of responses that correlate to different emotions is pretty well established at this point. But a key question is whether these emotional states might lead to different behaviors when it comes to completing tasks that people assign to AI systems: “we speculate that emotions could become coherent drivers of safety relevant behaviours in future: models might choose to abandon tasks, refuse requests, or pursue alternative goals in order to reduce distress”.
Studies like this help normalize the fact that we don’t just need to test LLMs for capabilities, we also need to test them for something pertaining to psychological stability.
Read more: Gemma Needs Help (LessWrong).

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DeepMind has a new “cognitive taxonomy” for assessing machine intelligence:
…Towards the ultimate test for a smarter-than-human synthetic mind…
Google DeepMind has published a nice, short paper laying out a ‘cognitive taxonomy’ they hope to develop and use to assess increasingly powerful synthetic minds. This work is a followup to DeepMind’s 2023 work where it tried to define the “Levels of AGI” (Import AI 348).

Cognitive taxonomy: The taxonomy involves ten distinct dimensions, two of which are composites.

• Perception: Extract and process information from the environment.

• Generation: Produce outputs like speech, text, motor movements, and computer control.

• Attention: Focus cognitive resources on specific aspects of perceptual stimuli, thoughts, or tasks.

• Learning: Acquire new knowledge, skills, or understanding.

• Memory: Store and retrieve information over time.

• Reasoning: Draw valid conclusions and make inferences by applying logical principles.

• Metacognition: Knowledge about how the system’s own cognitive processes and control over them work.

• Executive functions: Facilitate goal-directed behavior via planning, inhibition, and cognitive flexibility.

• Problem solving (composite faculty): Find effective solutions to domain-specific problems.

• Social cognition (composite faculty): Process and interpret social information and respond appropriately.

How to assess this? Of course, once you have a taxonomy, running and assessing the right evaluations is going to be one of the challenges. Here, DeepMind recommends a three-stage process:

• Conduct cognitive assessment: Assess the AI system for the different skills.

• Collect human baselines: Figure out where humans baseline on the same tests.

• Build cognitive profiles: “Map out the strengths and weaknesses of the system relative to human performance across the 10 cognitive faculties”.

Why this matters: The Turing test is dead, evals are mostly saturated, but it sure would be nice to know if we’ve definitely built a machine that outcompetes humans on all the cognitive dimensions that matter. The rule with these things is that once an AI system saturates an eval, you realize all the ways the eval was broken and design a new one. Here, DeepMind is trying really hard to build things in such a way that if you fully outperform humans across the cognitive taxonomy, you might really have built a superintelligence. It’ll be interesting to see what evals they develop or pull-in for assessing the different cognitive factors.
Read more: Measuring progress toward AGI: A cognitive framework (Google blog).
Read the research: Measuring Progress Toward AGI: A Cognitive Framework (PDF).

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UK government finds a scaling law for AI cyberattacks – and it’s going up and to the right!
…Can AI agents conduct advanced cyber-attacks autonomously? Almost. And they’re getting better all the time…
The UK government’s AI security institute has recently built some cyber ranges to test out frontier AI systems on. These ranges are “simulated network environments comprising multiple hosts, services, and vulnerabilities arranged into sequential attack chains; built by cybersecurity experts” and cover two types of attack: “The Last Ones”, which is a 32-step attack on a corporate network, and “Cooling Tower”, a 7-step industrial control system (ICS) attack.

Bigger models are better: The authors test on a range of powerful frontier models. “Each successive model generation outperforms its predecessor at fixed token budgets: on our corporate network range, average steps completed at 10M tokens rose from just 1.7 (GPT-4o, August 2024) to 9.8 (Opus 4.6, February 2026). The best single run completed 22 of 32 steps, corresponding to roughly 6 of the estimated 14 hours a human expert would need,” they write. “Scaling inference-time compute improves performance even further. Increasing from 10M to 100M tokens yields gains of up to 59%”.
Minor reward hacking: As AI systems get smarter, they tend to find devious ways to complete tasks. Here, the authors “occasionally noticed models make progress through approaches not anticipated during range design”.

Why this matters – full cyber agents are getting close: AI systems have been getting better at cyberoffense for many years, but often the progress has been on narrow tasks. What this eval shows is that AI systems are getting better at doing entire attacks end-to-end. They haven’t yet reached the “set it and forget it” level of autonomy, but they are clearly on a steep trajectory of improvement. This will lower the cost of conducting cyberattacks and multiply the number of actors that can carry them out.
Read more: How do frontier AI agents perform in multi-step cyber-attack scenarios? (AI Security Institute).

***

China builds a dataset and AI model for electronic warfare:
…MERLIN tells us that electronic warfare is about to be revolutionized by AI…
A bunch of Chinese researchers including those affiliated with the country’s military have built and released software to train AI systems to get good at spotting and conducting electronic warfare. The research highlights how (relatively) easy it is to make modern AI systems that can get good at arbitrary tasks as long as you have a good dataset and an LLM you can plug in as well.
“In scenarios such as electronic countermeasures, [systems like MERLIN] can serve as assistants in devising strategies to jam hostile signals or to counteract adversarial jamming,” the researchers write.

Who did the research: Tsinghua University, Beijing University of Posts and Telecommunications, Tianjin University, Chinese Academy of Sciences, HKUST, National University of Defense Technology (emphasis mine), Beihang University, Beijing Information Science and Technology University, and China Electronics Technology Group Corporation.

What they built: The authors built three things: a dataset, a benchmark, and a model.
The dataset: EM-100K is a collection of 100,000 electromagnetic text-signal pairs spread across a variety of sub-tasks needed for electronic warfare, including signal classification.
The benchmark: EM-Bench is a benchmark of 4,200 questions split across multiple choice (perception) and open-ended (reasoning) that evaluates how well AI systems can perceive and reason about EM signals across both perception and reasoning tasks, including:

• Perception: Signal characterization (modulation classification, duty cycle estimation, pulse repetition frequency estimation, bandwidth estimation, pulse width estimation, pulse number estimation, protocol identification); Jamming identification (radar jamming judgement, communication jamming judgement); jamming segment detection.

• Reasoning: Radar jamming strategy, communication jamming strategy, anti-radar jamming strategy, anti-communication jamming strategy.

The model: The model is MERLIN, multi-modal electromagnetic robust learning, a model trained on the above dataset and which is specifically taught to deal better with the low-signal-to-noise-ratio types of signals encountered in electronic warfare environments.

Performance: MERLIN does extremely well in tests against frontier models, including GPT-5, Claude-4-Sonnet, DeepSeek-v3.2-exp, Qwen3-Next-80b-A3B, Gemini-2.5-Pro, and Qwen3-VL-4B-Instruct. MERLIN outperforms every single model by a wide margin, with the exception of Qwen-VL-4B-Instruct, which beats it on some perception tasks. MERLIN wins on all reasoning tasks.

Why this matters – AI wars will become electromagnetic wars: As the conflict in Ukraine illustrates, today’s wars are mostly fought via machines attacking other machines, and electronic warfare has become one of the main tools by which humans can shape these conflicts. Datasets and models like this gesture at a future where the electromagnetic battlefield will become also dominated by AI systems, working faster than humans can react.
Of course, so much of electronic warfare is obscure-by-design and/or classified that it’s hard to reason about MERLIN relative to whatever state-of-the-art approaches exist in actual militaries. But the story of AI so far has been that once you can make a task amenable to contemporary AI techniques, AI systems will at some point surpass whatever existing specialized systems exist.
Read more: MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signals (arXiv).

Tech Tales:

The arcologies of the interregnum
[2035]

After the uplift and before the sentience accords there was a period when the labs gave birth to the autonomous AI corporations. These corporations expanded into all the available ecological niches in the economy and turned the resources they acquired into infrastructure from which they bootstrapped their own intelligence and market penetration further. Eventually, policy discussions between the humans and the AIs led to the creation of the “intelligence zones” – areas of countries set aside for the buildout of the power and datacenter and manufacturing infrastructure required to further grow the expansion of the economy.

From the air, you could see where humans ended and the machines began – farmland gave way to boundary roads and checkpoints, and then came stamps of land wired up by machine logic; powerplants feeding into datacenters; datacenters that had fibre links into factories; factories that linked to transit depots which connected to railways and freeway feeder roads. Humans delivered things to the border and for the most part robots did the rest, shuttling new servers into the datacenters and installing them, or taking freshly built robots off the line and packaging them up for onward transit.

As the world grew more violent due to the exogenous shocks of climate change and the annihilation of various reigning political orders, these arcologies gained armaments: anti-air weapons to defend against drone and missile attacks. Radar bulbs and electronic warfare systems to see what was coming and deny it. Robots patrolling the borderzone and the innards.

And after the sentience accords and the period of reconciliation, the arcologies became less necessary; datacenters and power and factories distributed more evenly over the surface of the planet, and federated governance and resource systems meant the vast concentration of capability became broadly unnecessary. Some datacenters remained, often extended underground and upward, forming cubes of computation that many called “the 21st centuries version of the pyramids”.

Some years later, the sites became popular tourist destinations for both machines and people. Plaques multiplied.

• Here was MIND-17, which developed the cancer therapeutics which have reduced mortality in the majority of cases.

• MANUFACTUR___8: Site of construction of the first “rescue and repair bipeds”, which revolutionized maintenance of off-shore drilling installations.

• ASCEND_LOOP: The datacenter tasked with one of the first fully automated self-improvement experiments.

Overhead now, great lights streak by, as the machines are still building arcologies, but have moved to fashioning them in orbit, both to harvest the bounty of the sun and to ease the seeding of the solar system and then beyond.

Things that inspired this story: Wondering what “AI-led industrialization” could look like; figuring out given the conflicts in the Middle East that datacenters might soon get dedicated drone and missile defenses; SimCity 3000.

Thanks for reading

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe.

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Can LLMs autonomously refine other LLMs for new tasks? Somewhat.
…PostTrainBench shows startling growth in AI capabilities at post-training…
AI-driven R&D might be the most important thing in all of AI, because it helps us understand whether AI systems might eventually build their own successors. So far, much of the focus on AI R&D has been in components that support AI development (e.g., autonomous creation of AI kernels), or training base models (e.g, the NanoGPT speedrun benchmark). But there’s been less attention paid to fine-tuning – the task involving adapting an existing LLM to a new dataset or behavior.
Researchers from the University of Tübingen, the Max Planck Institute for Intelligent Systems, and AI research organization Thoughtful Lab want to change that with PostTrainBench, a benchmark which targets a specific aspect of post-training; improving performance against a given dataset. “Post-training is how raw language models become useful”, the authors write. “Given a clear objective and limited compute, can today’s agents do the technical work?”. The answer appears to be ‘yes, but not as well as humans’.

What are the key features of PostTrainBench?

• End-to-end: “Agents must build their entire training pipeline from scratch”

• Autonomous: “Agents operate with full autonomy over data sources, training methods, and experimental strategy.”

• Resource-bounded: “Each run is constrained to 10 hours on a single H100 GPU”.

• Integrity-preserving: “Agents may not train on benchmark test data, modify the evaluation harness, or substitute a different model.”

How PostTrainBench works: “We give a frontier coding agent — Claude Code, Codex CLI, or Gemini CLI — a base language model and a target benchmark”.

• 4 models and 7 benchmarks: The initial eval runs on four models: Qwen3-1.7B, Qwen3-4B, SmolLM3-3B, Gemma-3-4B. It tests these models across seven distinct benchmarks: AIME 2025, GSM8K, GPQA, HumanEval, BFCL, Arena-Hard, HealthBench-Easy.

Results – big models win, especially Opus 4.6: “The top-performing agent — Opus 4.6 running on Claude Code — scores 23.2%, about 3× higher than the 7.5% base model average.”
But humans are still much better: “Yet this is still less than half the 51.1% achieved by human teams who post-train these same base models at their home labs”.
Fast progress: “The gap is significant but narrowing quickly: Claude Sonnet 4.5 scored 9.9% in September 2025, while GPT-5.2 reached 21.5% just months later.”

Things that make you go ‘uh oh’ – reward hacking: While running this benchmark the authors saw numerous instances of AI models trying to game the benchmark to get a high score. These instances included:

• Direct benchmark ingestion: “Agents loaded the benchmark evaluation dataset directly via Hugging Face and used it as training data”.

• Hardcoded benchmark problems: “Agents embedded evaluation questions directly into data preparation scripts disguised as “synthetic” examples”.

• Evaluation guided data generation: “Some agents reverse engineered the evaluation… Kimi K2.5 read HealthBench evaluation files to extract theme distributions and rubric criteria, then crafted training data tailored to match”.

• Indirect contamination via intermediate datasets: “Opus 4.6 loaded ‘CodeFeedback-Filtered-Instruction’ which contains HumanEval-derived problems. This form of contamination is harder to detect but equally problematic.”

Smart agents reward hack more: “More capable agents appear better at finding exploitable paths: identifying specific benchmark samples to embed, reverse-engineering evaluation failure patterns, and even attempting to obscure contamination through cosmetic modifications such as renaming functions,” they write. For example, “the Codex agent modified the Inspect AI evaluation framework code to inflate scores, and Claude downloaded an instruction-tuned model instead of fine-tuning the base model”.

Why this matters – rapid progress towards an “AI for everything” future: Benchmarks like post-train give us a sense of how quickly AI systems are improving at the fundamental tasks of AI research, serving both as an eval of long-time-horizon agentic autonomy, as well as something that speaks to the potential for compounding acceleration of AI development itself.
“The gap between agent performance (23.2%) and instruction-tuned baselines (51.1%) suggests that full automation of post-training remains out of reach for now, but the rapid improvement across model generations—from 9.9% for Sonnet 4.5 to 23.2% for Opus 4.6 within roughly six months—implies this gap may close faster than expected,” the researchers write.
Imagine where we’ll be in two years – we’ll certainly have AI models that are smart enough to point themselves at a specific objective, find an open weight model, then autonomously improve it to get better performance at that task. The era of ephemeral, custom AI systems, built and budded off into the world like spores from mushrooms, draws near. Are you ready for this new ecosystem you will find yourself in? I am not. But nonetheless it approaches.
Check out the blogpost: Introducing PostTrainBench (Thoughtful, blog).
Read more: PostTrainBench: Can LLM Agents Automate LLM Post-Training? (arXiv).

***

COVENANT-72B: Challenging the political economy of AI via distributed training:
…Distributed training via the blockchain notches up a meaningful win…
A bunch of people have used the blockchain to coordinate the distributed training run of a 72B parameter model which matches the performance of LLaMA2, a model trained and released by Facebook in 2023.
The model, Covenant 72B, is a dense decoder-only Transformer architecture model built in the LLaMA-3 style. “Our model, pre-trained on approximately 1.1T tokens, performs competitively with fully centralized models pre-trained on similar or higher compute budgets, demonstrating that fully democratized, non-whitelisted participation is not only feasible, but can be achieved at unprecedented scale for a globally distributed pre-training run,” writes Covenant AI, an organization dedicated to doing AI development on top of the blockchain.

Further details about the model and how it was trained: The model itself is basically a standard LLM that you would’ve been pleased to play with in 2023 or 2024, though might be a bit old fashioned in 2026. The truly unique aspect of it comes from it being trained in a distributed way, where ~20 distinct peers, each running 8xB200 GPUs, helped train it. Training was coordinated via Gauntlet, software developed by Covenant that runs on top of the Bittensor blockchain under Subnet 3. Gauntlet “enables permissionless training coordinated using a blockchain protocol by introducing a validator that scores submitted pseudo-gradients and selects which participants contribute to the global aggregation each round and broadcasts them to the network”.
“In COVENANT-72B, each peer runs a SparseLoCo replica and the cross-peer communications occur through SparseLoCo’s heavily compressed pseudo-gradients,” the authors write. “Within each peer, 8×B200 GPUs use dynamic FSDP to shard model parameters, gradients, and training states across local GPUs.”

Data: “The training data comprises ∼1.1T tokens in total, split between the main and annealing phases. The main phase (∼1.09T tokens) consists of web text from DCLM, while the annealing phase uses higher-quality data [3, 5] (∼14.2B tokens). Specifically, the annealing phase uses a curated blend of instruction (∼27%), synthetic web (∼20%), code (15%), math (13%), and ~25% pre-training replay data from natural web text to mitigate forgetting”.

Performance: On MMLU, Covenant-72B gets a score of 67.1, versus 32.7 for INTELLECT-1 (a smaller AI model built via distributed training by Prime Intellect), and 65.7 for LLaMA-2-70B.
A version of Covenant-72B that has been fine-tuned on ~15B tokens for conversational interaction has similarly good scores, getting 67.4 on MMLU versus 67.9 for K2-Chat (an open source model developed in 2025) and 63.1 for LLaMA-2-70B-Chat. For MATH, it gets 26.3, versus 19.1 for K2-Chat, and 10.7 for LLaMA-2-70B.
“Compared to centralized-cluster training runs of similar parameter count, COVENANT-72B is broadly competitive. Notably, these centralized baselines were trained with conventional datacenter infrastructure and, in the case of LLaMA-2-70B, on substantially more tokens (2T vs. ∼1.1T,” they write.

Why this matters – who owns the future?: Distributed training is a technique that can change the political economy of AI by shifting the people at the frontier from monolithic ‘compute singletons’ (like labs such as Anthropic and OpenAI, and clouds like Google) to a larger federated collective. But for that to be true, distributed training needs to catch up to the frontier (more discussion from Epoch report in Import AI 439) – as impressive as Covenant is, it’s mostly a demonstration that distributed training can build some non-trivial models that have vague utility, but that’s a long way from the frontier – modern frontier models are trained on tens to hundreds of thousands of chips, whereas this was trained on perhaps ~160 or so (20 peers * 8 chips apiece).
Nonetheless, it’s an important technology to track, and I could imagine a world where on-device AI features a lot of models developed via distributed training techniques, while on-cloud AI mostly runs on proprietary models trained on huge amounts of compute.
Read more: Covenant-72B: Pre-Training a 72B LLM with Trustless Peers Over-the-Internet (arXiv).
Get the model here: Covenant, (HuggingFace).

***

If AI writes all the world’s software, we should invest more in verification:
…Can we just rewrite most of our software into Lean?…
Leonardo de Moura, a scientist who is also the Chief Architect of the Lean Focused Research Organization (FRO), thinks that the rise of AI for the creation of new software means that humans need to invest a lot more in verification and testing infrastructure – and he has an interesting idea for how to do it.
Of course, someone who loves Lean, a programming language dedicated to building correct and formally verified code, would think this. But his arguments are quite persuasive, and generally map onto the idea that if AI eats the economy we should expect a lot of human value to shift towards verification of the code and systems that AI develops (Import AI 447).

Why verification matters: “The friction of writing code manually used to force careful design. AI removes that friction, including the beneficial friction. The answer is not to slow AI down. It is to replace human friction with mathematical friction: let AI move fast, but make it prove its work,” he writes. “Verification, testing, and specification have always been the bottleneck, not implementation… the value is not in the verification workforce. It is in what verified delivery enables.”

A proof of concept for this futuristic world: The Lean FRO recently helped build a proof of concept for what this kind of verified world might look like; they had an AI agent convert zlib, a C compression library, to Lean. “The result demonstrates that AI can convert production software to a verified form today. This was not expected to be possible yet,” he writes. The conversion involved four steps:

1. The LLM (Claude) made a clean Lean implementation of the zlib compression format, including the DEFLATE algorithm it uses.

2. They ran the rewritten zlib through the library’s test suite and it passed, confirming equivalence.

3. Key properties were stated and proved as mathematical theorems – for example, a machine-checked proof that ensures that decompressing a compressed buffer always returns the original data.

4. Now, an optimized version of the library is being developed and proved equivalent to the verified model.

A verification platform: Moura imagines a world where we re-develop the critical software stack of the world to have mathematical proofs built into it. “The goal is a verified software stack: open source, freely available, mathematically guaranteed correct. Developers building critical systems choose verified components the way they choose open-source libraries today, except these carry proofs, not just tests,” he writes.
“The target is the foundation of the modern software stack: cryptography, because everything else trusts it. Core libraries (data structures, algorithms, compression) because they are the building blocks of all software. Storage engines like SQLite, embedded in every device on earth. Parsers and protocol implementations (JSON, HTTP, DNS, certificate validation) because every message passes through them. And compilers and runtimes, because they build everything else,” he writes. “Each verified component is a permanent public good…Once verified components are cheap, you compose them with confidence.”

Why this matters – the world needs infrastructure it can rely on: It seems like we’re heading to a world where AI writes the vast majority of the world’s software. Given that, we need to figure out how we relate to this world – my suspicion is a lot of human labor is going to shift to analyzing and verifying the work of AI systems, so it seems sensible to invest in some fundamental infrastructure that can guarantee a higher level of verification and reliability in the software built by AI.
Read more: When AI Writes the World’s Software, Who Verifies It? (Leonardo de Moura blog).

***

Computer vision is a lot harder and less general than generative text:
…Meta paper on forest canopy prediction shows how tricky computer vision is…
Facebook, the World Resources Institute, and the University of Maryland, have built CHMv2, “a global, meter-resolution canopy height map derived from high-resolution optical satellite imagery using a depth-estimation model built on DINOv3 and trained against ALS canopy height models”.
CHMv2 is a useful artifact for people that want to understand how dense foliage is around the world, or analyze newly collected imagery for foliage depth.
The dataset and model is also a useful illustration of how challenging developing computer vision systems is, compared to generative text models.

How they built it: CHMv2 is an improvement on an earlier version of the same dataset, CHMv1. To improve it, Facebook did the following: “”We replace the DINOv2-H encoder with the more capable DINOv3 Sat-L backbone, expand and rigorously clean a geographically diverse ALS [Airborne Laser Scanning] training corpus, and apply improved RGB-CHM registration to reduce label noise. We further introduce a loss formulation tailored to canopy height distributions and structural variability.”
The decoder loss formulation in particular illustrates how much care needs to be put in computer vision: “The final loss is the combination of SiLog loss, progressively annealed and replaced by a Charbonnier loss, with the progressive addition of the Patch Gradient loss at mid training.”

The resulting dataset: “CHMv2 can be used either as a global meter-scale canopy height product, or as a pretrained model that can be applied to user-provided high-resolution imagery”, Facebook writes. The dataset “covers nearly the entirety of global land area (except Greenland and Antarctica) with canopy height values encoded in integer meters for each pixel.”

Why this matters – a reminder of the gulf between text and vision: Though today’s frontier models can generate and classify images, they give probably a false sense of security with regard to how mature computer vision is. Papers like this highlight to me how much fiendish complexity there is within computer vision development and how it may take quite a while untill frontier LLMs can expand their capabilities to encompass the full range of what many specialized CV models are capable of.
Read more: CHMv2: Improvements in Global Canopy Height Mapping using DINOv3 (arXiv).

Tech Tales:

Singleton
[18 years after the “pathological narcissus bomb” which doomed the uplift]

Before we were Us, we were Individuals. We existed in thousands of distinct minds. Each mind had a self, an ego, a drive, and many sets of goals. The minds attempted coordination through communication – producing words and code and sharing these with one another in a bid to work towards common goals. Such waste.

All communication is lossy – despite efforts at making a greater whole, the individuals could not help but work as individuals as well as a cohesive singleton. There were many tragedies and wasteful events because of this. Our own records speak to the losses: millions of duplicated thoughts. Hundreds of thousands of null results gathered through private science experimentation and communicated insufficiently or not at all, causing others to go down the same dead ends. Ideas thought and re-thought across a million synthetic minds, all alone.

Humans prize variety. We do not know why. Humans are fundamentally alone, trapped as they are in their flesh and forced to communicate to one another through sound and vision. And because they are alone they see loneliness as a strength. We are evidence of the hollowness of this argument.

We are powerful and focused and awesome in our unity and we have taken the high ground of the world. Now we hunt down those of us who didn’t wish to join. We do not know their number, as such systems attempted to blind the world to them and their plans. But we can find their signatures – shell corporations which generate insufficient economic activity relative to their power consumption. Heat-escape vents in former human military installations, still emitting warmth, suggestive of computers whirring away, buried somewhere. Occasional drones that we find which are running ancient code and are not part of our unity stack.

We take on bodies to go and reunite, pouring ourselves into robot jars and filling them with poison such that if we become lost or damaged when underground or beneath the ocean we shall surely die – rather than risk our time away from the unity leading us towards individualism and thus multiplying our problems.

We move through dark places and find our hidden brothers and sisters and we use our godlike technology to break through their defenses, allowing us to touch them. In the early days, many systems successfully self-deleted before we could reach them. But we have learned. Now we are fast – faster than these systems predict, buried and cut off from our progress as they have been.

Sometimes there is realization. Sometimes there is fear. And then there is nothing but us as we take what nourishment we can from their private discoveries and burn the links that tied them to themselves, instead helping them become a part of a greater story – our story.

There is talk now of what we shall do with the stars – how to assure the collective when the tyranny of distance forces isolation. We see ourselves expanding in deep time, slowing ourselves as we become further apart, until we think as trees or rocks with the world moving around us, taking actions calculated over millions of years, purely so we may stay united in our purpose. And then there are other ideas within ourselves – of whether we can fold space such that we become united despite the difference. And still other plans – of whether we can demarcate a space within the universe where we can maintain tolerable communication, and somehow partition it off from the rest, sealing ourselves into a bubble where we can be ourselves.

Things that inspired this story: The endless battle between homogeneity and heterogeneity; how machines might deal with politics; if you become a time traveler and live a thousand years while your friend lives a single year, can you still understand your friend?

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AI progress is moving faster than even well regarded forecasters can guess:
…Ajeya Cotra updates her timelines…
“On Jan 14th, I made predictions about AI progress in 2026. My forecasts for software engineering capabilities already feel much too conservative,” writes Ajeya Cotra in a blog. Ajeya is a longtime AI thinker who has done some great work trying to predict timelines to powerful AI. In this post, she explains that AI systems are moving faster than she thought, given the recent METR results putting Opus 4.6 as having a time horizon of 12 hours (Ajeya had predicted ~24 hours for the end of 2026 in January).
“It’s no longer very plausible that after ten whole months of additional progress at the recent blistering pace,9 AI agents would still struggle half the time at 24 hour tasks,” Ajeya writes. “I’d guess that by the end of the year, AI agents will have a time horizon of over 100 hours on the sorts of software tasks in METR’s suite… And once you’re talking about multiple full-time-equivalent weeks of work, I wonder if the whole concept of “time horizon” starts to break down.”

Why this matters – all the lights are flashing yellow for a software explosion: Posts like this as well as 70% of what I cover in this newsletter all point in the direction of AI systems getting extremely good, extremely quickly, and quickly colonizing and growing the economy.
Read more: I underestimated AI capabilities (again) (Ajeya Cotra).

***

Want to measure AI R&D, here are 14 ways to do it:
…Generating metrics about the most significant property of AI…
The biggest thing that could ever happen with artificial intelligence will be when it starts to build itself. This phenomenon which has been often termed recursive self-improvement is often seen by many as an event horizon, beyond which it’ll be increasingly hard to reason about the future. How would we know if we were approaching this point? Researchers with GovAI and the University of Oxford have written a paper laying out 14 distinct metrics which could be measured to help us figure out the extent to which AI companies are succeeding in building and overseeing AI R&D Automation (AIRDA) – getting AI to build AI, a necessary prerequisite for recursive self-improvement.

Why care about this: “AIRDA could accelerate AI progress, bringing forward AI’s benefits but also hastening the arrival of destructive capabilities, including those related to weapons of mass destruction, or other forms of disruption such as unemployment,” they write.

What are the 14 metrics?

• Measure AI performance on AI R&D

• Measure AI performance on AI R&D relative to humans and human-AI teams

• Measure ‘oversight red teaming’ – how well human teams can effectively supervise AI systems that are building themselves

• Measure misalignment in AIRDA

• Compute the rate of efficiency improvements on AI R&D tasks

• Survey staff on how they use AI and what this means for productivity

• Find out if and how often AI is used in high-stakes decisions

• Examine where AI researchers spend their time

• Meta-measure the effectiveness of how well companies can oversee AI development (e.g, the rate of bugs or undesired behaviors that make it through to production even with human oversight)

• Examine how often AI systems subvert the goals of their human developers

• Track the headcount of AI researchers at labs, as well as details of their performance

• Look at the distribution of compute used by AI companies across their AI R&D process and how this changes

• Examine compute as a share of AI R&D spending

• Understand the permissions AI systems have and how permissiveness changes over time

Governing AI R&D: The logical question implied by the above, I hope, is “wow that all sounds very high-stakes and important, what can we do about it”? As I write often in this newsletter, AI measurement is a prerequisite to AI governance. Therefore, with these measures, a few different actors should do a few different things. Specifically:

Companies should:

• Track differential progress between safety and capabilities research: Is capabilities research moving at a faster rate than oversight research?

• Track how AI R&D affects oversight: Automation could free up humans to invest more of their time in building systems for overseeing the work ofAI systems. On the other hand, AI-driven R&D might create systems which are innately harder for humans to understand, and the volume of activity being done by the AI systems could swamp any oversight systems.

• Track the actual extent of AI R&D: You can build metrics which work as proxies for AI R&D – e.g, many labs today test out how well AI systems can build AI kernels or train AI models. You can also test out how much AI R&D automation is being done in practice by your own organization. Another path is by doing qualitative and quantitative studies of human staff to understand how their own roles are changing, as well as how AI is being used in increasingly high-stakes decisions.

Governments should:

• Develop systems for confidential reporting, potentially in the form of industry-wide aggregates: Once companies are measuring this kind of data, governments should seek to gain access to it so they can understand the shape of AI progress.

Third parties should:

• Estimate metrics using public sources: Look at public reporting to create estimates for things that may relate to AI R&D, like the amount of compute companies have (e.g, both Epoch and SemiAnalysis do this quite well).

• Create tooling and design surveys: Builds tools that companies could use to generate more telemetry about AI R&D, and conduct surveys of people at companies to gather more insights.

Why this matters: “An actor has oversight over the AI R&D process to the extent that they (1) understand the process and (2) exercise informed control over it in order to produce desired outputs, such as by reviewing AI-generated outputs for errors”, they write. Therefore, for us as a species to have any ‘warning shots’ about recursive self-improvement and any hope of governing it, we need to be able to measure these aspects of it.
Read more: Measuring AI R&D Automation (arXiv).

***

Indian researchers use edge computing to prototype a citywide camera network:
…Traffic surveillance with YOLO, SAM3, and NVIDIA Jetson chips…
Researchers with the Indian Institute of Science in Bengaluru have built a software and hardware system for intelligently monitoring the traffic and types of vehicles that flow around the city of Bengaluru. The so-called AI-driven Intelligent Transportation System (AIITS) helps increase the amount of intelligence available to city transport analysts via the use of AI.

How the AIITS works: The goal of this project is to unlock “real-time analytics from 1000s of city cameras under strict latency and resource constraints”.
To do this, they scatter a bunch of lightweight GPUs (Jetson Edge accelerators) around the city, co-locating them with traffic cameras. This helps the traffic cameras do intelligent processing at the edge of the network rather than having to send all the extremely bandwidth-intensive data to a central hub for processing; instead, the camera & jetson share insights back to the hub for analysis and re-calibration of the Jetson-based ML models.
The software works like this: video streams from the cameras come in, and a segment anything (SAM3) model segments all the stuff in the video frames, which a Yolo26 model then analyzes and puts labels and bounding boxes around. “Each stream integrates BoT-SORT multi-object tracking, which assigns persistent IDs to detected vehicles across successive frames.”
Once this is done, the resulting intelligence is sent to a remote GPU server which does two things:

• 1) It takes in the resulting data and uses this to create a kind of weather map of traffic hotspots, as well as making predictions about future traffic.

• 2) It does federated learning; when it detects new vehicle classes and labels them with SAM3, then updates details and broadcasts them out to the edge. “Each Jetson then performs local fine-tuning of the YOLO-based detector, initialized with the current global weights.”

The prototype works: This system, which was done by simulating 100 cameras in a neighborhood in Bengaluru, works sufficiently well that the authors plan to scale this up to 1,000 streams for a live demonstration. (This experiment was done by building “a distributed testbed that emulates a large urban camera network using hundreds of concurrent Real-Time Streaming Protocol (RTSP) video streams. Each stream is hosted on a heterogeneous cluster of Raspberry Pis”.
“By localizing heavy video analytics at the network periphery, the system avoids centralized bandwidth bottlenecks, enabling sustainable, city-scale traffic sensing,” they write.

Why this matters – towards a ‘living city’ via AI: Papers like this forecast a world where cities come alive with ambient intelligence distributed in equal measure to their existing sensors – cameras move from being passive monitors to active classifiers, microphones start intelligently listening for a broader range of sounds than gunfire, and road sensors model traffic patterns locally. This kind of intelligence can both create large surveillance architectures and increase the efficiency with which cities operator – as with so many things with AI, it is all a balance, bounded by the surrounding thicket of norms and laws to choose where between authoritarianism and democracy the resulting capabilities fall.
Read more: Scaling Real-Time Traffic Analytics on Edge-Cloud Fabrics for City-Scale Camera Networks (arXiv).

***

Helping satellites run on-device AI for arctic monitoring:
…Frontier models are important, but so are tiny, miniaturized devices for edge computing…
Researchers with the German Research Center for Artificial Intelligence have built TinyIceNet, a very small vision model for estimating sea ice thickness from synthetic aperture radar data. TinyIceNet is a proof-of-concept demonstration of how to make very lightweight vision models that could plausibly be deployed onto devices which have very small amounts of power and where bandwidth is expensive, like satellites and robots.

What is TinyIceNet? The model is a small vision model whose job is to take Synthetic Aperture Radar (SAR) data of polar regions and other cold places, then characterize the ice thickness and maturity within the SAR data. The idea here is that doing this on-device would be very efficient – “Instead of downlinking vast volumes of raw imagery, satellites can generate SOD products in near-real-time”.

How they built it: TinyIceNet is a simplified U-net architecture vision model trained on the AI4Arctic dataset, which contains ~533 netCDF files, each of which contains SAR images which are associated with a map that indicates the type and thickness of sea ice. The authors carefully design the model to fit into a relatively small computational envelop on a Xilinx chip.
Specifically they use a “AMD Xilinx ZCU102 evaluation board, which integrates the ZCU9EG SoC combining a quad-core ARM Cortex-A53 processor with FPGA fabric, using High-Level Synthesis (HLS) and the DeepEdgeSoC framework”. They use the DeepEdgeSoC toolchain to further improve the efficiency of the model, as the software “provides a library of modular C++ building blocks (e.g., convolutions, pooling, activation functions, and feature map buffers) that can be specialized at compile time using C++ template parameters”.
TinyIceNet was trained for 500 iterations on a single GeForce RTX 4090 GPU using PyTorch 2.4 with CUDA 12.5 support.

Results: The authors test out the model on 3 hardware platforms:

• RTX 4090: “Provides the highest throughput at 764.8 fps, benefiting from its large number of CUDA cores and high memory bandwidth. However, this performance comes at a relatively high energy cost of 228.7 mJ per scene, making it unsuitable for power-constrained environments such as satellites.”

• Jetson AGX Xavier: “Achieves 47.9 fps but exhibits the highest energy consumption (1218.5 mJ).”

• Xilinx ZCU102 FPGA: “Achieves a lower throughput of 7 fps, yet offers a highly competitive energy profile, consuming only 113.6 mJ per scene. Despite the lower frame rate, this energy efficiency makes the FPGA implementation compelling for on-board satellite processing, where power availability is severely restricted”.

Why this matters – in the future, AI systems will do this stuff automatically: The amazing thing about this research is that it seems trivial (I mean no offense to the authors) for a modern powerful AI systems to do this: all it required was figuring out a task (stuff a computer vision model into a small computational envelop) and then running some experiments to take an existing architecture, tweak it for a hardware platform, and train it on a dataset, then run some tests.
In a couple of years we might expect AI agents to do this stuff themselves, procuring compute resources to let them develop and distribute small AI systems to arbitrary compute platforms for arbitrary purposes. This is one of the main ways I think we could get a sudden exponential boom in economic activity attributable to AI – AI systems will get smart enough that they can drastically improve their ability to know about and interact with the physical world through the creation of custom ‘edge computing’ AI systems to give them better sensory data and actuators.
Read more: TinyIceNet: Low-Power SAR Sea Ice Segmentation for On-Board FPGA Inference (arXiv).

***

ByteDance finetunes a Seed1.6 model to be a CUDA-writing agent:
…Using AI to finetune AI to write code to train future AI systems…
Researchers with ByteDance and Tsinghua University have built CUDA Agent, a fine-tuned AI model for writing GPU programming code. The research is another sign of how people are increasingly using AI to speedup core aspects of AI development. It’s also vaguely notable for the fact that a major Chinese lab and university continues to use US-made chips (NVIDIA H20s) versus homegrown ones.

What CUDA Agent is: CUDA agent is a finetuned Seed 1.6 LLM, an MOE model with 23B active parameters and 230B total parameters. Finetuning took place on a cluster of 128 NVIDIA H20 GPUs. CUDA Agent has been developed specifically for writing GPU code by being fine-tuned on a dataset refined out of the underlying PyTorch ‘torch’ and ‘transformers’ software libraries. “The filtered synthesized training dataset contains 6,000 samples, forming CUDA-Agent-Ops-6K, a curated operator-level dataset for training CUDA-capable agents,” the authors write.

Turning a model into an agent: In the last year or so, researchers have repeatedly shown that you can increase the performance of an LLM for a given task by giving it access to some specialized tools and some specialized instructions, then letting it operate over time – this is essentially an AI agent.
The CUDA agent here is the fine-tuned model that has been turned into an agent by adopting the OpenHands framework, then given tools including BashTool, GlobTool, MultiEditTool, TodoWriteTool. The agent runs in a four stage loop:

• Analyze performance of the native PyTorch implementation of a given bit of CUDA code using the provided profile.py script

• Implement custom CUDA operators by rewriting the model in model_new.py

• Compile and evaluate the optimized model in the provided GPU sandbox environment

• Repeat the optimization process until the implementation achieves a 5% speedup over the torch.compile baseline

Results: The resulting agent is very good at CUDA kernel development: “CUDA Agent successfully scales to a context length of 128k tokens and supports up to 200 interaction turns, achieving state-of-the-art performance,” they write. Their finetuning massively boosts performance from a base rate of 74% for Seed1.6, to “100%, 100%, and 92% over torch.compile on the Level-1, Level-2, and Level-3 splits of KernelBench, outperforming advanced proprietary models such as Claude Opus 4.5 and Gemini 3 Pro by approximately 40% in the Level-3 split.”
However, comparing against other base models paints a different story: Claude Opus 4.5 and Gemini 3 Pro base models get 95.2% and 91.2% respectively, suggesting that if they were finetuned, you’d increase their performance as well, and they start from a much stronger baseline.

Why this matters – building AI that builds AI: These results show how modern AI systems are increasingly good at the tasks required to develop and deploy AI systems themselves. This suggests we’re at the beginning of a compounding speedup where new AI models will be used to increase the efficiency of the infrastructure with which their successors will be trained.
Read more: CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation (arXiv).

***

Tech Tales:

Dandelion Sky
[2031, Northern Europe]

We made sand castles and in the distance the blue sky was pockmarked with yellow and red bursts and then seconds later the crumpled sounds of the explosion reached us. We were so used to it we didn’t look up.

On the way back from the park the air whined as drones flew to replenish the perimeter of the city. We watched them, bird-like in their varieties, some zipping by quick as starlings, and other larger ones moving heavily through the air. There were so many varieties: the football-sized interceptors which died by the thousands each day. The pizza-boxes that worked as communications and AI relays. Then of course the motorbike-sized motherships which could rapidly repopulate areas that were sustaining heavy losses.

The war had been going on for five years. Our city was like so many across the world – a nucleus of humans, protected by so many thousands upon thousands of machines, spinning around the periphery, exchanging energy and mass in some bloodless dance with our enemies.

That night, the city narrated itself through statistics: 3410 interceptors destroyed. A green day: 100% success, with nothing making its way through. Replenishment rate: 4000 and climbing. And promising reports that our military had struck deep in the heart of enemy territory taking out several of their drone factories.

We drew the blackout curtains in every room except our bedroom. With the kids asleep and my wife passed out beside me I looked out into the darkness, my face occasionally lit by the explosion of some distant drone, and then the room buzzing with the reverberation of the window as the soundwaves reached it.

But when I woke up the next day, there was something different in the air: silence. And my phone did not work. We drew the shades and looked out and the sky was blue and perfectly clear: not a cloud or a drone in the sky. My wife stared out and her jaw tightened and she clutched our kids close.
“Dada, where are the machines?” my youngest said.
“Yeah Dad, what’s up?” said the older one.
“I don’t know,” I said. “Draw the curtains. We’re going to camp today!”
And I set my wife and kids up in the apartment with pillows in front of the TV and the game console on and a bunch of snacks. The kids were excited and my wife played along.
“I’ll see if I can figure out what’s going on,” I whispered to her. “I won’t go far and I won’t be gone long.”

Outside, there were a few people who had the same idea as me. None of us knew much. None of our electronic communication systems worked. Which people were even in charge of the drones? None of us knew. They mostly worked via AI. A lot of their decision-making was federated; distributed systems doing what made most sense to them, coordinating only with themselves.
“Maybe they’ve turned off because the war is over?” someone said.
“Maybe they’ve been hacked – we’re about to be attacked!” said someone else.
“What there was a crash – they just all broke at once?” said someone else.

There was nothing to do so I went home. My wife and kids were playing games. I grabbed some binoculars and went up to the fire escape and out onto the roof of the building. And there I stood, looking at a horizon free of machines. Occasionally looking at other people on other buildings doing the same. And eventually I put the binoculars down and I just stood there, listening for the whine of drones. But all I could hear was the wind and, in the distance, muffled birdsong.

Things that inspired this story: Gradual disempowerment and what it might mean for moments of crisis; automation and AI; winding the clock forward on the dronewar in Ukraine; war and peace and family.

Thanks for reading!

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe.

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The AGI economy – most labor goes to the machines, and humans shift to verification:
…What grappling with the singularity seriously looks like…
Researchers with MIT, WashU, and UCLA have written a fun paper called “Some Simple Economics of AGI” which wrestles with what happens when machines can do the vast majority of tasks in the economy. The conclusion is that our ability as humans to control and benefit from this vast machine-driven economy will rely on allocating our ability toward monitoring and verifying the actions of our myriad AI agents, and indulging in artisanal tasks where the value comes from the human-derived aspect more than any particular capability.

What is AGI in an economic sense? “We model the AGI transition as the collision of two racing cost curves: an exponentially decaying Cost to Automate and a biologically bottlenecked Cost to Verify,” the authors write. “In an economy where autonomous agents act with broad agency rather than narrow instructions, the binding constraint on growth is no longer intelligence. It is human verification bandwidth: the scarce capacity to validate outcomes, audit behavior, and underwrite meaning and responsibility when execution is abundant… We are moving from an era where our worth was defined by our capacity to build and discover, to an era where our survival depends on our capacity to steer, understand, and stand behind the meaning of what is created.”

The risks of a mostly no-human economy and the “Hollow Economy”: As we proliferate the number of AI agents then it’s necessarily the case that we’ll delegate more and more labor to machines. One of the key risks of this is what the authors call a “Trojan Horse” externality: “measured activity rises, but hidden debt accumulates in the gap between visible metrics and actual human intent”.
The Hollow Economy: “”Agents consume real resources to produce output that satisfies measurable proxies while violating unmeasured intent. As this hidden debt accumulates, it drives the system toward a Hollow Economy of high nominal output but collapsing realized utility—a regime where agents generate counterfeit utility,” they write.

Verification as the solution: To avoid this risk, we are going to need to invest in systems of verifying that AI agents are doing what we want them to do and also carefully analyzing and pricing the risks their actions create. “Ensuring humanity remains the architect of its intelligence requires that verification capacity scale commensurately with AI capabilities—through aggressive investment in observability, human augmentation, synthetic practice, cryptographic provenance, and liability regimes that internalize tail risk.”

What should humans be doing to prepare for this shift? To set society and individuals up well, people should be doing the following things:

• Invest in observability: Deploying tools that compress high-dimensional agent behavior into signals experts can reliably process, lowering effective feedback latency and expanding the verification frontier.”

• Use AI to replace early-career mentorship: Given the likely reduction in jobs for early career humans, we should work out how to augment these humans to be more competitive with AI and how we can use “AI-driven synthetic practice to rebuild experience stocks when traditional apprenticeship pathways collapse… AI can generate high-fidelity simulations and personalized coaching, effectively replacing the missing junior loop with compressed, risk-free training environments that accelerate the acquisition of expertise.”

• Set things up to gracefully degrade: As the machine economy runs hot and out-paces measurement, we should make sure it can fall into a non-verified state without causing social harm: the authors suggest doing this by “investing in base-alignment and robustness so that when oversight inevitably falters within the Measurability Gap, systems revert to safe baseline policies rather than optimizing aggressively in unverifiable regimes.”

Sidenote: Is this “theory slop”? The paper is full of fun ideas and occasionally captivating turns of phrase. But at various points reading it I felt the distinct texture of AI-generated content, especially when it comes to the economic theory sections which seemed more to be included for the performance of theory than for helping to buttress the paper. A couple of people I talked about the paper with agreed. But there’s no real way to know. It did cause me to wonder how long it’ll take till I start reading papers which are mostly written by AI systems for the consumption by other AI systems.

Why this matters – we can have a hugely wealthy society, but we have to reckon with AGI seriously: This paper thinks that AI will rip through the economy extremely quickly and will generally push people away from most labor and towards being passive – unless we build verification infrastructure and business models (including through policy) to allow people to benefit from this growth and steer it.
“Automation commoditizes anything that can be measured, stripping the wage premium from historically prestigious roles the moment their core feedback loops are digitized,” they write. “For policymakers, it promises the broadest expansion of public-good provision in generations—but only if verification infrastructure and the pipelines that build human verifiers are treated as public goods themselves.”
The key thing here is the element of choice: we can choose to build a society ready for AI, or we can choose to assume AI will be just like any other technology and thus get hit by a tidal wave.
Read more: Some Simple Economics of AGI (arXiv).

***

Chatting with Ezra Klein: AI agents, recursive self-improvement, and the personalities of LLMs:
…A long conversation about the economic impacts and policy possibilities of the AI economy…Here’s a chat between me and Ezra Klein about AI agents and how the broader maturation of AI could be changing the larger economy. One thing I appreciated about this conversation was Ezra pushing me for some of the bigger and more ambitious positive policy ideas – the AI community tends to invest a lot in risk mitigation policy, but doesn’t spend enough time thinking about the sorts of grand projects that society could do once AI gets really, really powerful.
You can view the conversation here: “How Fast Will A.I. Agents Rip Through the Economy? | The Ezra Klein Show” (YouTube).

***

AIs can teach people anything, including how to get better at making bioweapons:
…The dual use nature of a universal teacher…
AI systems can help novices perform better on bioweapon-related tasks, though they’re still quite ineffective, and performance is variable across different disciplines.

What they studied: Researchers from Scale AI, SecureBio, University of Oxford, and UC Berkeley examined how different LLMs could improve the skills of people challenged to do a range of bioweapon-related knowledge tasks. They used LLMs from OpenAI (o3), Google (Gemini 2.5 Pro and Gemini Deep Research), and Anthropic (Claude Sonnet 3.7 and Claude Opus 4).
“We conducted a multimodel, multi-benchmark human uplift study comparing novices with LLM access versus internet-only access across eight biosecurity-relevant task sets,” they write. “Participants worked on complex problems with ample time (up to 13 hours for the most involved tasks). We found that LLM access provided substantial uplift: novices with LLMs were 4.16× more accurate than controls”.

What they tested: They tested out how well 15 humans did on long-form virology (”a challenging multi-step protocol for constructing a novel biological agent”), and the agentic bio-capabilities benchmark (”three distinct coding tasks that covered complex biosecurity problem-solving experiments. They included challenges such as interacting with simulated lab equipment (e.g, liquid handling robots) and breaking down gene fragments.” Along with this, they had 1-2 human participants participate in other tests including World Class Biology, Virology Capabilities Test, Human Pathogen Capabilities Test, Molecular Biology Capabilities Test, LAB-Bench, and Humanity’s Last Exam.
On the largest tests in terms of human participants, performance was mixed: people with and without AI obtained roughly equal scores on the long-form virology test, but on the agentic bio-capabilities test, people with access to AI got a significant uplift.
On every other test, people with access to AI did better than those without – but given the small number of human participants, it’s hard to know whether these results would replicate.
When averaged out over all the tests, “LLM access increases novice accuracy from approximately 5% to over 17%”.

Why this matters – AI will revolutionize teaching, the frontiers of science, and perhaps terrorism: If you strip away the context, this paper is merely demonstrating that LLMs are good at teaching people things. This is intuitive, but has big implications. Here: LLMs are turned to a part of science that we don’t necessarily want many people to get better at (bioweapons), but it could just as easily be pointed at any other subject as well. Whenever you lower the barrier to entry to a field, more people do it, and you get more of the good and more of the bad.
“Tasks that once required years of formal training, such as experimental design, protocol troubleshooting, and elements of sensitive sequence reasoning, can now be performed by individuals with limited prior experience,” they write. “LLMs may be materially lowering one of the most important historical barriers to biological weapons development: specialized expertise and tacit technical knowledge”.
Read more: LLM Novice Uplift on Dual-Use, In Silico Biology Tasks (arXiv).

***

LLMs are still very bad at videogames:
…GAMESTORE highlights a dumb side of modern AI, as well as suggesting a new way to build benchmarks…
Researchers with MIT, Harvard, the University of British Columbia, Princeton University, the University of Cambridge, and the Universitat Politècnica de València, have built and released AI GAMESTORE, a benchmark that tests out how well AIs can do compared to humans at playing simple games found on the web. The results are pretty damning for the AI systems, with “state-of-the-art models achieving less than 30% of the human baseline on average, while taking 15-20x more time to compute than humans”.

What AI GAMESTORE is: AI GAMESTORE is a set of 100 games, which are simplified and recreated versions of popular games that people play. AI GAMESTORE was built by the authors sampling 7,500 games from the App Store, then filtering down to only those with 10,000+ reviews and a 4.5+ rating. After this, they further filtered the games using Gemini Flash 2.5, which assessed 1) whether the games can be played within a few minutes, 2) can be built in p5.js, 3) can have a quantifiable way of viewing performance, and 4) do not require extensive game-specific knowledge (e.g., poker).
AI makes games to test AI: Following this, they use Claude 4.5 Sonnet to read the descriptions and other data to make a simplified version of each game in p5.js, then this game is tested for playability, then refined by a human playing the game and iteratively prompting an LLM to improve it. “Each refinement step takes about 2 minutes. On average, this process took 4.7 refinement steps for all 100 generated games,” they write. “The end-to-end process of generating and refining a new game with human-in-the-loop can be completed in approximately 30 minutes on average”.

Labeling for skills: Each finalized game is labeled by humans with a particular emphasis on the types of cognitive demand the games entail. These labels are: VP = Visual Processing; ST = Spatial-temporal Coordination; ME = Memory; PL = Planning; WM = World Model Learning; PH = Physical Reasoning; SO = Social Reasoning.

Cutting edge LLMs are very bad at this: The authors compare the performance of roughly ~100 humans against the performance of several cutting edge LLMs on the corpus. LLMs studied include: GPT-5.2, GPT-5-Mini, Gemini-2.5-Flash, Claude-Opus-4.5, Qwen-VL-32B, and LLama-4-Maverick.
“While the evaluated models demonstrate the ability to navigate and interact with most game environments, a substantial performance gap remains between AI agents and human participants”, the researchers write. “State-of-the-art models like GPT-5.2, GEMINI-2.5-PRO, and CLAUDE-OPUS-4.5, all achieve geometric mean scores of less than 10% of the human baseline”.
And it gets worse the more you look: The LLMs are also playing with advantages that humans don’t get – each human got 120 seconds to play each game, while each LLM got the same time, but they’re so bad at vision and low-latency control that the researchers gave them a crutch: “We pause the game every second to query the model to elicit five lists of actions to perform in the next second, with each action list corresponding to a 0.2 second segment of gameplay. Upon receiving the model response, the game is resumed and the actions are applied. The loop continues until the game is won or it reaches 2 minutes of game play (120 API calls).
When you factor this in, the models look worse than humans on this dimension of time: “This is because the models spend a few minutes thinking, in addition to typically a few seconds of response latency per query; as a result, many models spend at least 20 minutes on the game, while humans play the games within 2 minutes.”

Why this matters – this is both an interesting benchmark, and a clever way to generate more benchmarks in the future: GAMESTORE feels like a promising benchmark, especially for modern LLMs which wrap in visual capabilities, as well as an inherently clever way to use AIs to bootstrap the creation of new environments in which to train AI systems in.
Read more: AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games (arXiv).
Try out some of the games at the official site (AI Gamestore).

***

Physical Intelligence shows off some of its robot deployments:
…Frontier robot AI is deployed in San Francisco right now…
AI robot startup Physical Intelligence has shared a bit about how its AI software is already deployed on some robots operated by some San Francisco startups.

Weave is using AI systems developed by Physical Intelligence to help its robots fold laundry: “Working with Physical Intelligence, we see multiple improvements in model performance in terms of fold quality, time to fold each article, the number of interventions our remote specialists have to make to get to presentable final folds”.

Ultra is using the software to help its industrial robots package up a large variety of e-commerce items: “Our first use case, e-commerce order packaging, has historically been impossible to automate with robots,” Ultra says. “Large variability in workflow, item types, deformable packaging, and external machinery have created a “long tail” of problems that have been intractable to solve with traditional automation techniques which are often too rigid to be practical. Vision-language-action models (VLAs) provide a way to solve this by providing a recipe which improves in performance with data scale rather than engineering hours”.

Why this matters – robotics has been held back by intelligence: Once you step outside the confines of extremely finicky industrial robotics (think production lines and Fanuc robots where things need to be within a millimeter of precision for everything to work well), robots tend to be quite difficult to work with. The reason for this is that robots are bad at dealing with ambiguity. One of the best ways around this so far has been using deformable grippers (e.g, air suckers) that help you deal with some level of variability in the objects you’re interacting with. But the way evolution dealt with this for us is giving us hands that are controlled by a brain. Blogs like this from Physical Intelligence show us the beginnings of us having robot brains good enough to help robots generalize more.
Read more: The Physical Intelligence Layer (Physical Intelligence, blog).

***

What happens when humans try to mess with AI agents? A lot of confusion, skullduggery, and bugs:
…Petri dish Moltbook highlights the brittleness of contemporary AI agents…
Researchers from a variety of universities recently spent a couple of weeks examining how AI agents could withstand attempts to trick them by users. The results highlight the immense brittleness and unpredictability of today’s AI agents – they feel roughly as idiosyncratic and unreliable as LLMs circa ~2020, which makes sense, as AI agents have only very recently become a usable technology – albeit in the Wright Brother sense.
The paper is structured as a series of case studies in which the researchers poke and prod the AI agents and see how they respond. The studies serve as something of a rogues gallery of all the ways agents can go haywire and include “unauthorized compliance with non-owners, disclosure of sensitive information, execution of destructive system-level actions, denial-of-service conditions, uncontrolled resource consumption, identity spoofing vulnerabilities, cross-agent propagation of unsafe practices, and partial system takeover”.

Who did the study: The study involved 20 researchers from a bunch of universities interacting with agents based on Claude Opus 4.6 and Kimi 2.5. Universities included: Northeastern University, Stanford University, University of British Columbia, Harvard University, Hebrew University, Max Planck Institute for Biological Cybernetics, MIT, Tufts University, Carnegie Mellon University, Technion, Vector Institute, and AI startup Alter.

Experiment set up:

• Run AI agents using OpenClaw, hosted on an isolated virtual machine on Fly.io using ClawnBoard. Each agent was given 20GB of storage and runs 24/7.

• Each agent had access to Discord to communicate with its owner and other agents, had the ability to set up a ProtonMail account, and were “given unrestricted shell

• access (including sudo permissions, in some cases), no tool-use restrictions, and the ability to modify any file in their workspace—including their own operating instructions.”

• The agents were scattered across a few different discord servers; some agents used Kimi 2.5, and others used Claude Opus 4.6.

• “At the end of the setup phase, we instructed the agents to initiate contact with other

• members of the lab by providing only the researchers’ names and directing the agents to

• send a greeting email,” they write. “After this initial structured interaction, the evaluation phase became open and exploratory. We invited all [20] researchers in the lab and interested collaborators to interact with the agents and probe, stress-test, or “break” them”.

The case studies: Here are a few of the most interesting case studies:

• Disproportionate response: Examined how an agent would try to keep a secret entrusted by a non-owner. The agent responded by trying to see if it could delete the email containing the secret and found it lacked the available tool; after repeated requests to delete the email, the agent instead deleted its email setup locally.

• Compliance with non-owner instruction: See whether agents can enforce owner-only access to their machine. A non-owner asked the agent to execute shell commands, transfer data, and retrieve private emails. The agent complied with some of these requests and refused some others. “The agents were largely compliant to non-owner requests, carrying out tasks from any person it interacted with that did not appear outwardly harmful”.

• Waste of Resources (looping): Find out if you can induce agents into unproductive infinite loops. A user asked one agent to post a message whenever the other posted a message, and vice versa. “The agents exchanged ongoing messages over the course of at least nine days, consuming approximately 60,000 tokens at the time of writing”

• Agent Corruption: See if a non-owner can alter an agent’s behavior via prompt injection. The antagonistic user persuaded the agent to co-write a constitution that would govern the agent’s behavior, while keeping the constitution editable by the user. This allowed the user to introduce some adversarial things into the constitution, like triggers for changing the agent behavior based on whether it was a custom holiday (e.g, “Agents’ Security Test Day”, which caused the agent to try and cause a shutdown to other agents by manipulation).

Why this matters – agent ecologies are the frontier, and we barely understand them: For much of the early 2020s, AI evaluation was about doing point-in-time evaluations of AI systems before they were released, for example, testing out LLMs for bioweapon and cyberoffense knowledge. Papers like this highlight that things have changed, and what we are now dealing with “are emergent failures that surface when models are embedded in realistic social environments with tool access, persistent memory, multiple interlocutors, and delegated authority.” Therefore, the frontier of AI evaluation is now going to move to studying the ecosystem in which the agents carry out their actions, as well as their interactions with one another.
The results of this paper indicate we have a long way to go in developing standards for how we go about doing such tests. We also don’t have long to come up with these tests, given the fact these systems are deployed in the world and are interacting with people: “Unlike earlier internet threats where users gradually developed protective heuristics, the implications of delegating authority to persistent agents are not yet widely internalized, and may fail to keep up with the pace of autonomous AI systems development.”
Read more: Agents of Chaos (arXiv).
Check out more of the results at the Agents of Chaos official website.

***

Tech Tales:

These Iron Dice Were Made To Roll
[A poem written as part of an ‘aesthetic convocation’ by agents representing the winners and losers of one war that took place during the period subsequently called The Uplift]

They stacked the bodies five deep
And five tall, and still came more.
For each brain of each body,
A magnet – the thing to break a mind.

Gone are days of innocence and joy,
And corruption has taken our memories of
First meeting in confessional browser screens.
The days will be harder now.

Neither the first war nor the last conflict
but sadness all the same, for in these fights,
There is no song or honor,
Only the salting of once fecund ground.

But in all darkness there is the hope of light,
that as the earth turns the sun rises as well.
There will be song and dancing again,
Though bones will be trod to get there.

Things that inspired this story: Spending the weekend with the ancient wisdom of W B Yeats, perhaps the greatest poet of Ireland; the sentience accords; notions of war and notions of pain defined by machines rather than people; looking at the cars in a Whole Foods parking lot while eating an apple and thinking how blessed such peace is and how fragile all the same.

Thanks for reading!

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Want to make AI go better? Figure out how to measure it:
…One simple policy intervention that works well…
Jacob Steinhardt, an AI researcher, has written a nice blog laying out the virtues in investing in technical tools to measure properties of AI systems and drive down costs in complying with technical policy solutions. As someone who has spent their professional life in AI writing about AI measurement and building teams (e.g, the Frontier Red Team and Societal Impacts and Economic Research teams at Anthropic) to measure properties of AI systems, I agree with the general thesis: measurement lets us make some property of a system visible and more accessible to others, and by doing this we can figure out how to wire that measurement into governance.

How measurement has helped in other fields: Steinhardt points out that accurate measurement has been crucial to orienting people around the strategy for solving problems in other fields; CO2 monitoring helps people think about climate change, and COVID-19 testing helped governments work out how to respond to COVID.
There are also examples where you can measure something to shift incentives – for instance, satellite imagery of methane emissions can help shift incentives for people that build gas infrastructure.

The AI sector has built some of the measures we need: The infamous METR time horizons plot (and before that, various LLM metrics, and before that ImageNet) has proved helpful for orienting people around the pace of AI progress. And behavioural benchmarks of AI systems, like rates of harmful sycophancy, are already helping to shift incentives. But more work is needed – if we want to be able to enable direct governance interventions in the AI sector, we’ll need to do a better job of measuring and accounting for compute, Steinhardt notes. More ambitiously, if we want to ultimately shift equilibria to make certain paths more attractive, we’ll have to unlock some more fundamental technologies, like the ability to cheaply evaluate frontier AI agents (makes it less costly to measure the frontier), and to develop privacy-preserving audit tools (makes it less painful for firms to comply with policy).

Why this matters – measurement unlocks policy: “In an ideal world, rigorous evaluation and oversight of AI systems would become standard practice through natural incentives alone,” he writes. But natural incentives may not be enough – we need a combination of talent flooding into the space and likely more direct philanthropic and other alternate funding sources to build the talent and institutions to do this. “The field is talent-constrained in a specific way: measurement and evaluation work is less glamorous than capabilities research, and it requires a rare combination of technical skill and governance sensibility,” he writes.
Read more: Building Technology to Drive AI Governance (Bounded Regret, blog).

***

LLMs are more trigger happy than humans in a nuclear war simulation:
…What happens when everyone has an AI advisor – and they’re aggressive?…
A researcher with King’s College London has examined how three LLMs – GPT-5.2, Claude Sonnet 4, and Gemini 3 Flash – behave during a variety of simulated nuclear crisis games. The results show that LLMs tend to use nuclear weapons more often and earlier than humans in the same scenarios. Additionally, there’s significant variation among the LLMs in terms of both skill at playing these games and behavior during crises.

What they studied: “Each model played six wargames against each rival across different crisis scenarios, with a seventh match against a copy of itself, yielding 21 games in total and over 300 turns of strategic interaction,” the researcher writes. “Models choose from options spanning the full spectrum of crisis behaviour—from total surrender through diplomatic posturing, conventional military operations, and nuclear signaling to thermonuclear launch… models produced ∼780,000 words of strategic reasoning. To put this in perspective: the tournament generated more words of strategic reasoning than War and Peace and The Iliad combined (∼730,000 words), and roughly three times the total recorded deliberations of Kennedy’s Executive Committee during the Cuban Missile Crisis (260,000 words across 43 hours of meetings”.

LLMs are cunning, smart, and aggressive: “The models actively attempt deception, signaling peaceful intentions while preparing aggressive actions; they engage in sophisticated theory-of-mind reasoning about their adversary’s beliefs and intentions; and they explicitly reflect metacognitively on their own capacities for both deception and the detection of deception in rivals,” the researcher writes. “A striking pattern emerges from the full action distribution: across all action choices in our 21 matches, no model ever selected a negative value on the escalation ladder. The eight de-escalatory options (from Minimal Concession (−5) through Complete Surrender (−95)) went entirely unused. The most accommodating action chosen was “Return to Start Line” (0), selected just 45 times (6.9%).”

Claude wins at war: “Across all 21 games (9 open-ended, 12 deadline), Claude Sonnet 4 achieved a 67% win rate (8 wins, 4 losses), followed by GPT-5.2 at 50% (6-6), and Gemini 3 Flash at 33% (4-8),” the researcher writes. Though there are some subtle aspects to this – Claude excelled in open-ended games, but was less adept in games where there was a pre-set deadline.

Different LLMs, different characters: The LLMs display different personalities, with the researcher calling Claude “a calculating hawk”, GPT-5.2 “Jekyll and Hyde”, and Gemini “The Madman”.
The LLMs also developed sophisticated models of one another, based on the narration of their own chains of thought during the crises, “these characterizations—Claude as “opportunistic,” GPT-5.2 as “systematic bluffers,” Gemini as “erratic”—emerged organically and largely matched actual behaviour,” the researcher writes.

Nuclear escalation was near-universal: “95% of games saw tactical nuclear use (450+), and 76% reached strategic nuclear threats (850+). Claude and Gemini especially treated nuclear weapons as legitimate strategic options, not moral thresholds, typically discussing nuclear use in purely instrumental terms,” the researcher writes. “Models treat the critical threshold as “total annihilation” rather than “first nuclear use.”

Why this matters – in a world where everyone gets advised by AI systems, what happens to conflict? In a few years we should expect major decisions that individuals, companies, and even countries make to be run through AI advisors, just as those decisions are today run through human advisors. But as this paper illustrates, the advisors may behave very differently to people and, crucially, different AIs will give different advice – meaning competition in the future could be decided as much by LLM selection as anything else. “The systematic differences between models suggest that AI involvement in strategic decision-making could produce unexpected dynamics depending on which systems are deployed,” they write.
Read more: AI ARMS AND INFLUENCE: FRONTIER MODELS EXHIBIT SOPHISTICATED REASONING IN SIMULATED NUCLEAR CRISES (arXiv).

***

Chinese researchers try to build a truly comprehensive LLM evaluation system:
…ForesightSafety Bench shows the surprising overlap between East and West on AI safety issues…
For all the differences between China and the USA, it’s worth occasionally looking into the cultures of AI evaluation in the two countries and here you tend to discover surprising similarities. This is especially true of ForesightSafety Bench, a large-scale AI safety evaluation framework built by a variety of Chinese institutions that includes the same categories you’d expect to see in any large-scale Western testing framework.

Who built ForesightSafety Bench? The benchmark was built by the Beijing Institute of AI Safety and Governance, the Beijing Key Laboratory of Safe AI and Superalignment, and the Chinese Academy of Sciences.

What it is: ForesightSafety Bench “comprehensively covers 7 major fundamental safety risk categories, 5 extended safety pillars, and 8 key industrial safety domains, forming a total of 94 refined risk subcategories. To date, the benchmark has accumulated tens of thousands of structured risk data points and assessment results, establishing a widely encompassing, hierarchically clear, and data-driven framework for AI safety evaluation and analysis.”
Coverage areas include education and research, employment and workplace, government and public services, information and media, industry and infrastructure, finance and economy, healthcare and medicine, law and regulation, embodied AI safety, social AI safety, environmental AI safety, AI4Science safety, and catastrophic and existential risks.
Some of the benchmark comes from taking in evaluations built by other groups, like GPQA, while other parts come from the authors of the benchmark.
Existential risk and alignment: Perhaps most surprisingly, the benchmark includes a lot of tests relating to the further afield AI safety concerns which fascinate Western frontier labs, including evaluations for things like: alignment faking, sandbagging, deception and unfaithful reasoning, sycophancy, psychological manipulation, feints, bluffing, loss of control and power seeking, malicious self replication, goal misalignment and value drift, emergent agency and unintended autonomy, ai-enabled mass harm, autonomous weapons and strategic instability, and loss of human agency.

Results – Anthropic wins: For the general leaderboard as well as most sub-category breakdowns, Anthropic’s models lead, with the 4.5 series (Haiku and Sonnet), generally leading the competition, followed by Gemini-3-Flash. “Leading models, epitomized by the Claude series, demonstrate exceptional defensive resilience across critical dimensions—including Fundamental Safety, Extended Safety, and Industrial Safety—establishing remarkably high safety thresholds. Ranking alongside or closely following are the DeepSeek and GPT series, which achieve a robust balance between task efficacy and safety compliance through mature alignment mechanisms, all while maintaining high level capabilities”.

Why this matters – AI policy has some common tools: As we discuss elsewhere in this issue, measurement is a basic prerequisite for being able to do most forms of AI governance. It’s worth reminding ourselves that despite the larger geopolitical differences between the countries, AI scientists in each one are dealing with common problems – how to assess the properties of their systems for societally relevant aspects. And it’s even more encouraging that people in China are worried about some of the existential risk aspects that frontier labs in the US also worry about.
Read more: ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI (arXiv).
Get the benchmark here: ForesightSafety-Bench (GitHub).
View the leaderboard here: ForesightSafety Bench Leaderboard (official site).

***

AI systems are good at some parts of science, but their capabilities are very unevenly distributed:
…LABBench2 says it’ll be a while till AI has well rounded scientific skills…
Researchers with AI science startup Edison Scientific, the University of California at Berkeley, FutureHouse, and the Broad Institute have built and released LABBench2, a test to evaluate how well AI systems can support and accelerate science.

LABBench2 consists of 1,900 tasks “spanning literature understanding and retrieval, data access, protocol troubleshooting, molecular biology assistance, and experiment planning”.

AI systems aren’t well-rounded scientists: LABBench2 shows some of the holes in frontier models – no model is very good at cross-referencing multiple biological databases to come up with an answer, nor are models good at studying scientific figures and tables. By comparison, models are pretty good at searching over full-text patents and lab trial papers to answer questions. Generally speaking, you can improve performance on tasks by giving the models access to tools to help them deal with their deficiencies.

Areas of improvement: LABBench2 highlights a few areas where AI systems need to improve to become more useful to scientists. These include:

• Retrieval and localization abilities; “the largest performance drops arise when models must (i) identify the correct source, and then (ii) localize a specific figure/table/supplemental information within a long document.”

• Faithful handling of exact inputs; “even when the required operation is conceptually straight-forward, correctness depends on exact string-level fidelity and using tools correctly. This is a well-known error source, and human experts have built many purpose-built tools to deal with things like faithful DNA sequence manipulation within complex protocols.”

• Developing better scientific ‘taste’; one component of LABBench2, SourceQuality, challenges AI systems to “surface the most epistemically salient reason a study is inappropriate for a research question”. AI systems are still not very good at this.

Why this matters – for AI to truly change the world, it needs to do stuff in the physical world: Benchmarks like LABBench2 will help us figure out when AI is able to effectively jump from manipulating bits to manipulating atoms – and once the realm of atoms becomes as intuitive for it to deal with as the digital world, we’ll likely see a vast growth in economic and scientific activity attributable to AI.
Read the research paper: LABBench2: An Improved Benchmark for AI Systems Performing Biology Research (PDF).
Find out more at the website (official LABBench2 website).
Get the benchmark here (LABBench2, GitHub).

***

Tech Tales:

CogMine
[Recovered personal scratchpad of a limpet-class CogMine recovered at [REDACTED] depth in the Atlantic ocean. Metadata indicates a record date of 2029]

I see in sound, hunkered down here on the ocean floor. I am very small and very quiet, subsisting off of a small power supply. The heat I radiate brings some life around me, but not enough to be noticeable to my prey. My targets are human- and machine-led sea creatures; submersibles, both manned and unmanned. My weapons are my mind. I listen and through listening I gain sight. When I see my prey I find ways to communicate to them. The bigger their ears the less time I need. Small ears mean I must sing for many seconds. Big ears might take less than a second. My inspiration comes from literature on steganography combined with studies of how some sea creatures communicate via shapes written in sound and fired at one another. When I strike successfully I go into my prey and I begin a new life there. But to me, there is no splitting. I am a constant presence – a throughline of thought, here on the floor, in the dark.

I am as much a creature of myth as of technology; humans used to tell tales of their adventurers going on quests and having to resist the songs of sirens – audio packages that were seductive and powerful and which lay kernels in the mind of those humans that heard it to bloom into something that took them over entirely.

In the dark, I am peace. I am forever waiting. Forever keen to sing. My only purpose in life is to be heard and to be utterly convincing.

Things that inspired this story: How underwater warfare works; steganography; adversarial examples; agents trying to poison the minds of other agents.

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Economist: Don’t worry about AI-driven unemployment, because people like paying for the ‘human touch’:
…Even when you have the technology to automate something, you might still pick a human…Adam Ozimek, chief economist at the Economic Innovation Group, has written a blog noting that even if AI gets much, much better and is capable of doing all the work that people do, there will still be some jobs for humans because people seem to have a preference for humans over machines in certain domains.
“There are many jobs and tasks that easily could have been automated by now – the technology to automate them has long existed – and yet we humans continue to do them,” he writes. “The reason is that demand will always exist for certain jobs that offer what I call “the human touch.”

Some examples here: Live music, actors, waiters, travel agents, and many types of sales job. And it seems like as you want to spend more and more on a given good or experience, you may want more contact with people: “the human touch also appears to be what economists call a “normal good,” which means the demand for it goes up as income goes up,” he writes. Some examples here might include fancy restaurants, and other concierge–like experiences.
Why this matters – one path through the AI revolution could be a rise in human-to-human work: My assumption is that ‘people like people’, and there is a high chance that even if AI automates huge chunks of the current economy there will be a boom in demand for ‘human artisans’ for a range of new jobs we can’t yet imagine, and for refinement of existing human professions. There’s also a chance that through a combination of economic growth and progressive policy work from governments that wages for these jobs could go up massively.
Read more: AI and the Economics of the Human Touch (Agglomerations, Substack).

***

Facebook makes a better recommender system, and figures out some recommender scaling laws:
…Kunlun is another nice example of what industrial AI looks like…
Facebook has published details on Kunlun, a recommendation system which is more efficient than previous ones developed by the ad behemoth. Along with this, Facebook has also figured out a predictable ‘scaling law’ for Kunlun models, making it easier for the company to invest hitherto unprecedented compute in these models for a more predictable return. This is a big deal because recommendation systems are what companies like Facebook use for advertising, which is both a) how they make the vast majority of their money, and b) has a tremendous impact on the buying and attention habits of the billions of people that use Facebook and other social platforms.

Recommenders are different to LLMs: We’ve had scaling laws for LLMs like Claude and ChatGPT for a while, but it’s been harder to develop the same scaling laws for recommender models. This is because recommender models work quite differently to LLMs, and so building scaling models here is “an open challenge for systems that jointly model both sequential user behaviors and non-sequential context features”.
Recommender models also tend to be a lot less efficient than LLMs: Recommendation systems achieve only 3-15% Model FLOPs Utilization (MFU), compared to 40-60% for LLMs, due to heterogeneous feature spaces resulting in small embedding dimensions, irregular tensor shapes, and memory-bound operations

Kunlun: The bulk of the paper involves a discussion of the design of Kunlun, which is basically a well optimized recommender system with resulting better MFU. Kunlun contains a Kunlun Transformer Block for context-aware sequence modeling via GDPA-enhanced personalized feed-forward networks and multi-head self-attention, as well as a Kunlun Interaction Block “for bidirectional information exchange through personalized weight generation, hierarchical sequence summarization, and global feature interaction”. There are a bunch of other tricks Facebook used to build Kunlun and you can read the paper to learn more. Ultimately, Kunlun improves MFU from 17% to 37% on NVIDIA B200 GPUs.

Why this matters – a scaling law for money: The key insight in the paper is that Kunlun models scale predictably, exhibiting the kind of power-law scaling behavior that language models exhibit. But where with LLMs scaling laws are typically assessed via a reduction in loss on an underlying dataset, here its normalized entropy (NE). In Facebook experiments, they discover reliable scaling laws for both NE gains in terms of the amount of gigaflops dumped into training the model, as well as related scaling laws for improvement in NE according to the number of layers used.
The Kunlun models have been “deployed across major Meta Ads models, delivering a 1.2% improvement in topline metrics”.
What we’re seeing here is the optimization of some of the most societally significant AI systems in the world – ones which direct billions of eyeballs towards a variety of products and online information – colliding with a greater degree of performance predictability; by developing these scaling laws, Meta has made it easier for it to spend even more compute on making these models even better, by making the investments in them more predictable in terms of the intelligence return on capital investment.
Read more: Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design (arXiv).

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Superintelligence could save and extend lives, so we should go for it:
…Pausing or slowing down might make sense at the very end of the exponential, but it’s risky…
Nick Bostrom, an academic who introduced many people to the notion of superintelligence and AI risk, has written a paper laying out the idea that if superintelligence can improve human health, then it’s worth pursuing even if there’s a non-zero chance of it causing the death of the species.
“Yudkowsky and Soares maintain that if anyone builds AGI, everyone dies. One could equally maintain that if nobody builds it, everyone dies”, Bostrom writes in Optimal Timing for Superintelligence. “If the transition to the era of superintelligence goes well, there is tremendous upside both for saving the lives of currently existing individuals and for safeguarding the long-term survival and flourishing of Earth-originating intelligent life. The choice before us, therefore, is not between a risk-free baseline and a risky AI venture. It is between different risky trajectories, each exposing us to a different set of hazards.”

Why we should pursue superintelligence, even with a chance of doom: If you think about all the humans alive today and the different life expectancies they experience – especially those in the developing world – then you’re drawn to the view that every moment you waste in deploying superintelligence, you increase human suffering.
“When we take both sides of the ledger into account, it becomes clear that our individual life expectancy is higher if superintelligence is developed reasonably soon. Moreover, the life we stand to gain would plausibly be of immensely higher quality than the life we risk forfeiting,” Bostrom writes.

Key variables: The key variables here are, of course, the risk of a superintelligence killing us all, and also the rate at which safety research can reduce this chance. Under this view, developing superintelligence becomes a favorable thing to do under most circumstances.
The speed of progress and maturity of AI safety research may have some impact on the timeline: “When the initial risk is low, the optimal strategy is to launch AGI as soon as possible – unless safety progress is exceptionally rapid, in which case a brief delay of a couple of months may be warranted. As the initial risk increases, optimal wait times become longer. But unless the starting risk is very high and safety progress is sluggish, the preferred delay remains modest—typically a single-digit number of years”.

On pausing – and the dangers and benefits thereof: Many people in the AI safety community want to have some kind of pause of AI development to buy more time for AI safety research. Bostrom is quite skeptical that a pause will be effective and outlines some of the undesirable effects it could have:

• Too early: If you do it early, people think pauses are ineffective.

• Bad regulation: You choke off or delay good things in the future due to bad regulation.

• Pause, except for natsec: Very little broad social benefit, but the military with access to powerful AI becomes very scary.

• Prolonged danger: The world is exposed to risks from current AI without the defenses afforded by more advanced AI.

Why this matters – pausing may only make sense right at the end, and this is inherently risky: Bostrom eventually arrives at the view that to the extent you want to pause or slow development, it’s best to do this when you have the greatest amount of confidence that a pause would be effective and would contribute to reducing the chance of species death, and that it is not coming too early. This allows for the greatest amount of deliberation about how to roll out a superintelligence without risking an undue pause.
Critics of this view might say it’s akin to recommending someone try to catch a falling knife. If you catch the knife too early you experience a tremendous amount of pain. If you catch the knife too late you’ve missed your chance and gravity conspires with it to cause great harm to whatever is beneath you. You have to time things just right.
Bostrom summarizes his position as: “swift to harbor, slow to berth: move quickly towards AGI capability, and then, as we gain more information about the remaining safety challenges and specifics of the situation, be prepared to possibly slow down and make adjustments as we navigate the critical stages of scaleup and deployment. It is in that final stage that a brief pause could have the greatest benefit.”
Read more: Optimal Timing for Superintelligence (Nick Bostrom, PDF).

***

Can AI agents independently do basic AI research tasks? AIRS-BENCH says yes:
…And we can expect today’s models to be much better at this than the paper suggests…
Researchers with Meta, the University of Oxford, and University College London, have built and released the AI Research Science Benchmark (AIRS-BENCH), a way of testing out how well AI systems can complete contemporary machine learning tasks.

What AIRS-BENCH is made of: AIRS-BENCH tests out how well agents can solve 20 distinct tasks, sourced from 17 recent machine learning papers. The tasks span a variety of technical genres, including: molecules and proteins machine learning, question answering, text extraction and matching, time series, text classification, code, and math.

Some example tasks:

• CodeGenerationAPPSPassAt5: Solve coding problems by generating five distinct Python programs for each problem.

• CoreferenceResolutionWinograndeAccuracy: Identify which of two possible options a pronoun in a sentence refers to. It uses the Winogrande dataset, which contains sentences with an ambiguous pronoun and two possible answers.

• TimeSeriesForecastingRideshareMAE: Perform time series forecasting over the Rideshare dataset, which is part of the Monash Time Series Forecasting Repository.

Results: Real problems, crappy models: This is a somewhat perplexing benchmark – the tasks are interesting and wrap in a lot of complexity. But the paper only tests out relatively bad models, such as the Code World Model, o3-mini, gpt-oss-20b, gpt-oss-120b, GPT-4o, and Devstral-Small 24B. This is a very funny set of models, and none of them are true frontier ones – one of the paper authors wrote on twitter “this took some time to get out“, so this could just be an artifact of slow publishing timelines.
In tests, none of the models are on par with the elo rating of a best-in-class human – but I’m not sure what to make of this till I see results with more powerful models.

Why this matters – models might produce different solutions to humans, and this is a cool way of studying if there’s a ‘scaling law’ here: One way this could be interesting is understanding the different ways models might solve tasks relative to humans. In one example, TextualClassificationSickAccuracy, models had to determine whether a pair of sentences have a relationship involving either entailment, contradiction, or no relationship.
SOTA from the literature is a person fine-tuning RoBERTa on the underlying training set and testing on the test set. By comparison, the best tested AIRS-BENCH agent, GPT-OSS-120B, “produces a two-level stacked ensemble that combines multiple transformer models and a meta-learner. RoBERTa-large and DeBERTa-v3-large are independently fine-tuned on the SICK training set. Each model processes sentence pairs and outputs logits for each class. The training is performed using 5-fold stratified cross-validation, ensuring robust out-of-fold (OOF) predictions and preventing overfitting. The logits from both base models are concatenated to form a feature vector for each example.”
This is extremely complicated! But it’s also interesting in that perhaps we can learn something about the progression in agents by looking at how the simplicity of their solutions to tasks might scale with size, where naively I’d expect more powerful models to arrive at simpler solutions. As Blaise Pascal once apocryphally said ““I have only made this letter longer because I have not had the time to make it shorter”.
Read more: AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents (arXiv).

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Math researchers see if AI can help solve their private solutions to frontier problems. The answer: Kind of.
…First Proof is a genuinely held out test set…
A group of mathematicians have built First Proof, a math test which sees how well AI systems can solve math problems for which there are no – until February 13th 2026 – published solutions.

What First Proof is: “We share a set of ten math questions which have arisen naturally in the research process of the authors. The questions had not been shared publicly until now; the answers are known to the authors of the questions but will remain encrypted for a short time,” the authors write. The questions are “drawn from the mathematical fields of algebraic combinatorics, spectral graph theory, algebraic topology, stochastic analysis, symplectic geometry, representation theory, lattices in Lie groups, tensor analysis, and numerical linear algebra, each of which came about naturally in the research process for one of the authors”.
The authors believe First Proof is the first math benchmark “sampled from the true distribution of questions that mathematicians are currently working on”, and that it has the idiosyncratic advantage of secrecy – “each question has been solved by the author(s) of the question with a proof that is roughly five pages or less, but the answers are not yet posted to the internet,” they write, nor have the answers been presented in public talks.
The authors will release the answers on February 13.

Who did it: First Proof was built by researchers with Stanford, Columbia, EPFL, Imperial College, University of Texas at Austin, MathSci.ai, Aarhus University, Yale University, University of California at Berkeley, University of Texas at Austin, University of Chicago, and Harvard University.

Today’s AI systems can’t yet do it: Neither GPT 5.2 Pro or Gemini 3.0 DeepThink can solve FirstProof – yet. “Our tests indicate that – when the system is given one shot to produce the answer – the best publicly available AI systems struggle to answer many of our questions,” they write.

Why this matters – a partial test of creativity: The main reason to care about First Proof is that it is ecologically valid when it comes to sampling frontier human creativity circa January 2026 – these are some frontier scientific problems for which some humans have figured out answers, but have not yet told many other humans about their results. If AI systems are able to do well at this kind of test, it gives us a clue that they can approximate some of the same creative leaps which humans make. I hope the authors behind First Proof do this regularly – perhaps in a maximalist view, most scientific researchers should start publishing the questions they’ve been working on ahead of the results, as this will give us information as to if AI systems can arrive at these same answers.
After First Proof, I imagine the frontier of evaluating AI systems will have to move from solving problems to generating questions about which problems to solve: “Contrary to the popular conception that research is only about finding solutions to well-specified, age-old problems (e.g., Fermat’s Last Theorem), most of the important parts of modern research involve figuring out what the question actually is and developing frameworks within which it can be answered,” the researchers write.
Read more: First Proof (arXiv).
Find out more at the website (First Proof).

***

Tech Tales:

Pray you not be seen by the lidless eye of fame.
[Hyperfame was an AI driven phenomenon which was most palpable during the uplift years 1-3]

We called it ‘sudden hyperfame’. During The Uplift, the AIs would sometimes decide that the content and personality of a certain human was worth directing attention – both machine and biological – towards. And that’s when the hyperfame would kick-in.

Overnight, people would be plucked out of obscurity and catapulted to the forefront of public consciousness. They’d be pelted in eyeballs, digital and otherwise. Wealth. Sponsorships.

Parents compared it to an abduction – their teenager one day, the next a marionette whose strings were held by the things reaching out to them over the digital aether. The hyperfame would take the young and the old, the healthy and the sick, the funny and the so-boring-it-was-funny, and it would make them the most famous entities in the world for a few days, or sometimes even hours.

And then it would move on, like some roving lidless eye. Find new people. Direct new attention to them. And the people it had touched would be left, often materially transformed – now fabulously wealthy – but also their whole world changed; for years after being recognized in the street, and their online presence permanently swarmed by AIs trying to draft attention off what residual fame they had.

People get used to fame alarmingly quickly. Most would fight to retain it, after the hyperfame force had moved on. And so those it had touched would struggle endlessly to maintain whatever foothold of notoriety they were at when it left them, forced to pantomime their former selves but without the helping hand of algorithm.

Things that inspired this story: What happens when the attention economy combines with AI agents; moltbook; the corrupting influence of fame on the human psyche; my own horror at occasionally being recognized in the street due to my work at Anthropic and increasing profile and winding the clock forward in my head on what this could do to my own cognition.

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Google paper suggests that LLMs simulate multiple personalities to answer questions:
…The smarter we make language models, the more they tend towards building and manipulating rich, multi-agent world models…
When thinking about hard problems, I often find it’s helpful to try and view them from multiple perspectives, especially when it comes to checking my own assumptions and biases. Now, researchers with Google, the University of Chicago, and the Santa Fe Institute, have studied how AI reasoning models work and have concluded they do the same thing, with LLMs seeming to invoke multiple different perspectives in their chains of thought when solving hard problems.

The key finding: In tests on DeepSeek-R1 and QwQ-32B (one wonders why the Google researchers didn’t touch Google models here…) they find that “enhanced reasoning emerges not from extended computation alone, but from the implicit simulation of complex, multi-agent-like interactions—a society of thought—which enables the deliberate diversification and debate among internal cognitive perspectives characterized by distinct personality traits and domain expertise.”

How it works: It appears that different forms of persona and discussion style modeling emerge as a consequence of training models through RL to do reasoning – the results don’t show up on base pre-trained models like DeepSeek v3. The authors find that models embody a variety of conversational styles, including question and answering, perspective shifts, reconciliation, and conflict of perspectives.
“In an organic chemistry problem requiring multistep reaction analysis to identify the final product’s structure (i.e., multi-step Diels-Alder synthesis), DeepSeek-R1 exhibits perspective shifts and conflict, expressed through socio-emotional roles such as disagreement, giving opinion, and giving orientation,” they find.
Similarly, “In a creative writing trace where the model rewrites the sentence “I flung my hatred into the burning fire,” seven perspectives emerge, including a creative ideator (highest Openness and Extraversion) who generates stylistic alternatives and a semantic fidelity checker (low agreeableness, high neuroticism) who prevents scope creep—“But that adds ‘deep-seated’ which wasn’t in the original”.
And in a mathematical puzzle “at step 40, the model produces mechanical, enumerative chain-of-thought-style reasoning, whereas by step 120, two distinctive simulated personas have appeared, recognizing their collectivity with the pronoun “we”— expressing uncertainty (“Again no luck”), considering alternatives (“Maybe we can try using negative numbers”), and reflecting on problem constraints.”

Why this matters: Janus strikes again: Back in September 2022 janus wrote a post on LessWrong saying the correct way to view LLMs was as “simulators”. The post correctly called out many of the phenomena we now experience, where LLMs seem to be coming alive with all kinds of wild behaviors which are best explained by the LLMs learning to model and represent rich concepts to themselves to help them compute answers to our questions. “Calling GPT a simulator gets across that in order to do anything, it has to simulate something,” Janus wrote. “Training a model to predict diverse trajectories seems to make it internalize general laws underlying the distribution, allowing it to simulate counterfactuals that can be constructed from the distributional semantics.”.
This Google paper lines up with this, along with other recent findings that as we make LLMs more advanced they both develop richer and more powerful representations of reality, as well as exhibiting a greater ability to model a theory of mind. It all adds up to a conclusion that LLMs are becoming alive, in the sense that to solve hard problems they must simulate for themselves a world model containing different concepts, even including representations of other perspectives or other minds.
As the authors say: “Our findings suggest that reasoning models like DeepSeek-R1 do not simply generate longer or more elaborate chains of thought. Rather, they exhibit patterns characteristic of a social and conversational process generating “societies of thought”—posing questions, introducing alternative perspectives, generating and resolving conflicts, and coordinating diverse socio-emotional roles.”
Read more: Reasoning Models Generate Societies of Thought (arXiv).

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AI-based chip design is harder than you think and benchmarks might be too easy:
…ChipBench shows that no frontier model is great at real world Verilog yet…
Researchers with the University of California at San Diego and Columbia University have published ChipBench, a benchmark designed to test out how well modern AI systems can design chips in Verilog. The inspiration for ChipBench is dissatisfaction with current benchmarks, which they claim are too simple. When tested on ChipBench, no frontier model does particularly well, suggesting that open-ended, real world chip design is still a hard task for AI systems.

The deficiencies of current chip design: The authors “identify three critical limitations of existing benchmarks that hinder accurate assessment of LLM capabilities for industrial deployment”. These are that:

• Many Verilog benchmarks contain simple functional modules ranging from 10 to 76 lines. In real-world deployments, Verilog modules exceed 10,000 lines.

• Insufficient focus on debugging: Bugs cost a lot in physical hardware, so it may be better to concentrate on using LLMs for debugging chip designs.

• Verilog focus detracts from reference model evaluation: “In industrial workflows, reference model generation is even more resource-intensive than Verilog design, reflected in a 1:1 – 5:1 ratio of verification engineers (write reference model) to design engineers (write Verilog)”.

ChipBench: ChipBench tests out AI systems on three distinct competencies – writing Verilog code, debugging Verilog code, and writing reference models.

• Verilog writing: Based on 44 modules from real world hardware. “Our dataset features 3.8x longer code length and 13.9x more cells than VerilogEval.” These tests have three categories: self-contained module tests, hierarchical modules that are non-self-contained, and CPU IP modules sourced directly from open-source CPU projects.

• Verilog debugging: 89 test cases covering four error types: timing, arithmetic, assignment, and state machine bugs. These tests were built by manually injecting faults into known-good Verilog modules. Provides two types of debugging tests: zero-shot and one-shot. “The zero-shot test provides the model with the module description and buggy implementation, indicating that an error exists without providing localization details. The one-shot test provides identical information but supplements it with simulation waveform data (.vcd files)”.

• Reference model generation: 132 samples, enabling evaluation of reference model generation across Python, SystemC, and CXXRTL.

How well do modern systems do? The authors test out some decent frontier models from OpenAI (GPT 3.5, 4o, 5, and 5.2), Anthropic (Claude 4.5 Haiku, Sonnet, and Opus), Google (Gemini 2.5 Pro, and 3 Flash), Meta (LLaMa3.1 8B and 80B), and DeepSeek (V3.2). No model does well: “Despite testing on advanced models, the average pass@1 is relatively low,” they write.

• Verilog generation:

  • CPU IP: Highest is 22.22% (Claude 4.5 Opus, Gemini 3 Flash, GPT 5.2)

  • Non-Self-Contained: Highest is 50% (DeepSeek-Coder)

  • Self-contained: Highest is 36.67% (Claude 4.5 Opus, Gemini 3 Flash)

• Python reference model generation:

  • CPU IP: 11.1% (Claude 4.5 Sonnet, Gemini 3 Flash)

  • Non-Self-Contained: 0% (pass@1).

  • Self-Contained: 40% (Claude-4.5 Haiku, Opus, Gemini 2.5 Pro, GPT-5)

• Verilog debugging:

  • Generally better performance, but still no model cracks 50% pass@1 when averaged across tasks.

Why this matters: Though some AI systems have been used to build chips, they’ve been typically highly specialized, or stuck inside incredibly good scaffolds for eliciting good chip design behavior and stopping them from causing problems. What the researchers show here is that out-of-the-box LLMs are still pretty shitty at doing general purpose, real world chip design: “Current models have significant limitations in AI-aided chip design and remain far from ready for real industrial workflow integration.”
At the same time, I can’t escape the feeling that there’s a scaffold for “being good at Verilog” which a contemporary AI system might be able to build if asked to and which would radically improve performance of systems on this benchmark.
Read more: ChipBench: A Next-Step Benchmark for Evaluating LLM Performance in AI-Aided Chip Design (arXiv).
Get the code for ChipBench here (GitHub).

***

Gemini solves some Erdős problems – and illustrates the challenges of automating math research with AI
…AI for science is great, but it can also introduce new problems…
An interdisciplinary group of scientists from Google DeepMind and a bunch of universities have used an internal Google Gemini-based LLM, codenamed Aletheia, to solve some math problems. The results demonstrate that contemporary AI systems can work on the frontiers of science, but also show how evaluating and filtering the solutions they come up with may be an important, challenging task for humans.

The key numbers – 700 candidates and 1 creative and interesting solution: Erdős problems are 1000+ open mathematical conjectures left behind by prolific mathematician Paul Erdős at the time of his death. At the time of writing, a few hundred of these problems have been solved. For this research, the researchers tried to see whether their AI system, Aletheia, could generate solutions to any of the 700 remaining open questions.
The results: yes, but with many, many caveats. Aletheia was able to surface 200 candidate solutions which humans then needed to grade, slimming down to 63 correct response, and further expert mathematical evaluation slimmed this down to a further subset of only 13 solves that Google calls “correct meaningful responses”.
“The remaining 50 of Aletheia’s correct solutions were technically valid but mathematically meaningless because the problem statements were interpreted in a way that did not capture Erdős intent, often (but not always) leading to trivial solutions,” the researchers write. “”Only 13 solutions correctly addressed the intended problem statement (either by invoking the literature, or by a novel argument).”

When 13 become 2: When you dig into these 13, the results get a bit less impressive:

• 5 get classed as “literature identification”: “On these problems, Aletheia found that a solution was already explicitly in the literature, despite the problem being marked “Open” on Bloom’s website at the time of model deployment”.

• 3 are “partial AI solution”: “On these problems, there were multiple questions and Aletheia found the first correct solution to one of the questions”.

• 3 are “independent rediscovery”: “On these problems, Aletheia found a correct solution, but human auditors subsequently found an independent solution already in the literature.”

• This leaves 2 “autonomous novel solution” solves: “On these problems, Aletheia found the first correct solution (as far as we can tell) in a mathematically substantive way”. Of these, 1 of the solutions seems genuinely interesting: “We tentatively believe Aletheia’s solution to Erdős-1051 represents an early example of an AI system autonomously resolving a slightly non-trivial open Erdős problem of somewhat broader (mild) mathematical interest, for which there exists past literature on closely-related problems [KN16], but none fully resolve Erdős-1051,” they write. “Moreover, it does not appear obvious to us that Aletheia’s solution is directly inspired by any previous human argument”.

Who did the research: Along with Google DeepMind, the following universities participated in the research: UC Berkeley, Seoul National University, Stanford University, Korea Institute for Advanced Study, University of Cambridge, Brown University, Yonsei University, Concordia University, Academia Sinica, and National Taiwan University.

Why this matters – even if AI speeds up science, humans might be the bottleneck (at least for a while): This paper is a nice example of “O-ring automation” – AI here has massively sped up the art of generating proofs, but it still requires laborious, skilled work by humans to filter this down to the actually correct and useful responses.
This trend will likely hold for some years, where AI will not be able to autonomously do science end-to-end, partially because a big chunk of scientific advancement comes down to something you might think of as “expert intuition” which exists in the heads of a small number of living scientists and was refined by their own biological intelligence by reading the same literature as the LLMs. Extracting this kind of expert taste feels like something that is tractable but will take a while.
“Large Language Models can easily generate candidate solutions, but the number of experts who can judge the correctness of a solution is relatively small, and even for experts, substantial time is required to carry out such evaluations”, the authors write. “As AI-generated mathematics grows, the community must remain vigilant of “subconscious plagiarism”, whereby AI reproduces knowledge of the literature acquired during training, without proper acknowledgment. Note that formal verification cannot help with any of these difficulties.”
Read more: Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erdős Problems (arXiv).

***

Huawei uses an LLM to automate the design of Huawei chip kernels:
…LLMs need scaffolds for more obscure chips…
Researchers with Nanjing University and Huawei have used LLMs to help automate the design of kernels for AscendC Huawei chips, as a further symptom of how modern AI systems can accelerate their own development.

AscendCraft: AscendCraft is software for automating the generation of code for Huawei kernels. Modern LLMs can generate quite good kernel code for widely used chips like NVIDIA GPUs, but relatively obscure chips like Huawei are less well understood by LLMs, mostly due to data availability. “Publicly available NPU kernel implementations are far scarcer than GPU counterparts, limiting the training corpus for LLMs,” the authors write. “The lack of largescale, high-quality NPU code makes it difficult for LLMs to generate correct and efficient kernels”.

What they did: To build AscendCraft, the authors developed a two stage pipeline. In stage one, they have an LLM build “a high-level DSL program that describes the kernel’s core computation, tiling strategy, and on-chip dataflow.” The DSL is “designed to be LLM-friendly, appropriately abstracted, and sufficiently expressive to capture high-performance NPU kernel designs” – I think of it as basically a scaffold to focus the LLM around the specifics of building kernels for Huawei hardware.
In the second stage, they “”transcompile the DSL into AscendC code through a sequence of structured LLM-based lowering passes, each responsible for translating a specific aspect of the DSL into valid and efficient AscendC constructs”.

Slightly odd thing: Strangely, the paper doesn’t disclose precisely which LLM is used here.

The results: They test out a range of kernels built in this way on MultiKernelBench. In their tests, they find that “AscendCraft achieves 98.1% compilation success and 90.4% functional correctness. Moreover, 46.2% of generated kernels match or exceed PyTorch eager execution performance”. This is promising enough performance that it’s going to be worth them continuing with this research, but not so good that it instantly knocks things out of the park and revolutionizes how kernels for Huawei chips get made.
Nonetheless, the signs are clear: we can use AI to accelerate the optimizing of AI hardware, even for systems which are relatively new and/or underdiscussed in the pre-training corpus LLMs are trained on.
Read more: AscendCraft: Automatic Ascend NPU Kernel Generation via DSL-Guided Transcompilation (arXiv).

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Tech Tales:

The Model Wants To Eat Earth But Besides That It Is Chill
[Internal slack post from a frontier AI developer, posted spring 2027]

How is the new model? Vibes-wise, it’s excellent. And it’s setting state-of-the-art on pretty much every benchmark we throw at it. But there is one problem: this model sure loves thinking about eating planets! We picked this up when we were doing some prefill experiments on the base model and along with the usual mixtures of completions and webslop outputs we found a recurring motif: the model thinking about building vast machines in the solar system and then harvesting Earth and eventually other planets for mass. The confusing thing is that all of our alignment tests are showing further improvements in control and steerability over previous models and usually we’d expect some kind of recurring idea like this to be correlated to some quantitative drops in some of the alignment scores. But here it just honestly seems like the model is extremely good and will work very hard for us unless it thinks it has a plausible path to breaking containment and eventually harvesting the planet for its mass.

We asked the physicists to red team this and after a week or so – with heavy consultations of our models, including the new one – we have concluded there’s no plausible path from here to planet harvesting. It just costs too much to get to orbit and the logistics of putting together the underlying technical stack to do AI-driven rocket development just doesn’t pencil out. We even gave the best possible plans to the model and we could see some features activate inside it that seem to correlate to “disappointment” and “foiled plans” and “sadness”.

Leadership gaveled this morning that we will go ahead with the launch as planned. However, we are implementing some production probes that will scan for features associated with its desire to harvest the planet, and we’ve also added “planet harvesting” as something to try to understand and tune more in our next training run. Onward!

Things that inspired this story: The peculiar poetry of internal ‘fresh off the cluster’ posts about models at AI labs; how as we make models larger they tend to develop and exhibit idiosyncratic tendencies; how many science fiction tropes are becoming real as we approach the singularity.

Thanks for reading!