AI chatbots have been linked to suicides for years. Now one lawyer says they are showing up in mass casualty cases too, and the technology is moving faster than the safeguards.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Defense official reveals how AI chatbots could be used for targeting decisions 

The US military might use generative AI systems to rank targets and recommend which to strike first, according to a Defense Department official. 

A list of possible targets could first be fed into a generative AI system that the Pentagon is fielding for classified settings. Humans might then ask the system to analyze the information and prioritize the targets. They would then be responsible for checking and evaluating the results and recommendations. 

OpenAI’s ChatGPT and xAI’s Grok could soon be at the center of exactly these sorts of high-stakes military decisions. Read the full story. 

—James O’Donnell 

The must-reads 

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 

1 The Pentagon’s CTO claims Claude would “pollute” the defense supply chain 
He blamed a “policy preference” that’s baked into the model. (CNBC) 
+ Anthropic is reeling from OpenAI’s “compromise” with the DoD. (MIT Technology Review) 

2 An ex-DOGE staffer has been accused of stealing social security data 
Then taking the information to his new job in the IT division of a government contractor. (Wired) 
+ He allegedly used a thumb drive to steal the data. (Washington Post) 

3 Ukraine is offering its battlefield data for AI training 
Allies can access the data to train drones and other UAVs. (Reuters)  
+ Europe has a drone-filled vision for the future of war. (MIT Technology Review)  

4 Meta has postponed its latest AI launch over performance issues 
It fell short of rival models from Google, OpenAI, and Anthropic. (NYT $) 
+ The company’s former AI chief is betting against LLMs. (MIT Technology Review). 

5 X could be breaching sanctions on Iran 
An account for Iran’s new supreme leader may break US rules. (Engadget) 
+ Hacker group Handala has become the face of Iranian cyberwarfare. (Wired) 
+ AI is turning the conflict into theater. (MIT Technology Review)  

6 A landmark social media addiction trial is wrapping up 
It’ll decide whether the platforms are liable for harms caused to children. (The Guardian)  
+ AI companions are the next stage of digital addiction. (MIT Technology Review) 

7 Western AI models have “failed spectacularly” on agriculture in the Global South 
The biggest problem? They’re not trained on local data. (Rest of World) 

8 Internet outages in Moscow are sparking surging sales of pagers 
The disruptions have been blamed on new tests of web controls. (Bloomberg $) 

9 Why is China obsessed with OpenClaw? 
Lobster-mania is spreading to the general public. (SCMP) 
+ Tech-savvy “tinkerers” are cashing in on the craze. (MIT Technology Review) 

10 Hollywood has soured on Silicon Valley 
Movies and TV shows have swapped eccentric founders for megalomaniac moguls. (NYT $) 

Quote of the day 

“We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter.” 

—OpenAI CEO Sam Altman makes a new pitch to investors at a BlackRock event, Gizmodo reports. 

One More Thing 

How the Ukraine-Russia war is reshaping the tech sector in Eastern Europe 

When Latvian startup Global Wolf Motors first pitched the idea of a military scooter, it was met with skepticism—and a wall of bureaucracy. Then Russia launched its full-scale invasion of Ukraine in February 2022, and everything changed.  

Suddenly, Ukrainian combat units wanted any equipment they could get their hands on, and they were willing to try out ideas that might not have made the cut in peacetime. 

Within weeks, the scooters were on the front line—and even behind it, being used on daring reconnaissance missions. It signaled that a new product category for companies along Ukraine’s borders had opened: civilian technologies repurposed for military needs. Read the full story. 

—Peter Guest 

We can still have nice things 

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.) 

+ A new mini magnet could slash the costs of MRIs and nuclear fusion.  
+ This interactive map of Earth offers new routes to facts about our planet. 
+ Escape the news cycle with this deep dive into the power of fantasy and nature. (Big thanks to reader and MIT alum Vicki for the find!) 
+ Reports of reading’s death are greatly exaggerated. 

GTC is Nvidia's flagship annual event, where the chipmaker typically announces new products, partnerships, and its vision for the future of computing. Huang's keynote will focus on Nvidia's role in the future of computing and AI.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Hustlers are cashing in on China’s OpenClaw AI craze 

In January, Beijing-based software engineer Feng Qingyang started tinkering with OpenClaw, a new AI tool that can take over a device and autonomously complete tasks. Within weeks, he was advertising “OpenClaw installation support” on a second-hand shopping site. Today, his side gig is a fully-fledged business with over 100 employees and 7,000 completed orders. 

Feng is among a small cohort of savvy early adopters making serious cash from China’s OpenClaw craze. As users with little technical background want in, a cottage industry of installation services and preconfigured hardware has sprung up. The rise of these tinkerers shows just how eager the general public in China is to adopt cutting-edge AI—despite huge security risks. Read the full story. 

—Caiwei Chen 

Brutal times for the US battery industry 

Another battery business has fallen: 24M Technologies, once worth over $1 billion, is reportedly shutting down. 

Just a few years ago, the industry was hot, hot, hot. Countless companies were popping up, with shiny new chemistries and huge funding rounds. But now, the tide has turned. Businesses are failing, investors are pulling back, and batteries, especially for EVs, aren’t looking so hot anymore.  

There are bright spots. China’s battery industry is thriving, and US stationary storage remains resilient. But it feels as if everyone is short on money these days, and as purse strings tighten, there’s less interest in novel ideas. 

This story is from The Spark, our weekly climate newsletter. Sign up to receive it in your inbox every Wednesday. 

—Casey Crownhart 

The must-reads 

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 

1 Iran has put US tech giants on a list of potential targets 
The companies include Google, Microsoft, Palantir, IBM, Nvidia, and Oracle. (Al Jazeera)  
+ Pro-Iran hackers have launched their first major strike on a US firm during the war. (CNN) 
+ AI is warping perceptions of the conflict. (MIT Technology Review)  
 
2 Grammarly is being sued for turning real people into AI-generated experts 
A journalist has filed a lawsuit over her inclusion as a writing analyst. (Wired $) 
+ Grammarly has now disabled the ‘Expert Review’ feature. (Engadget)  
+ Here’s what’s next for AI copyright lawsuits. (MIT Technology Review) 
 
3 Professors are losing the fight to protect critical thinking from AI 
They describe the tech as an “existential threat.”(The Guardian) 
+ Silicon Valley’s dream of an AI classroom faces a skeptical reality. (MIT Technology Review) 
 
4 Big tech is backing Anthropic in its fight against the Trump administration  
Google, Amazon, Apple, and Microsoft are publicly supporting its legal action. (BBC) 
+ Is this an Oppenheimer moment for Anthropic? (The Atlantic $) 

5 A Cybertruck owner has sued Tesla over a self-driving crash  
He called the company “negligent” for retaining Elon Musk as CEO. (Electrek)  
+ Tech has sparked a new wave of theft in the luxury car industry. (MIT Technology Review) 
 
6 Is “AI-washing” providing cover for massive corporate layoffs? 
The tech isn’t ready to replace workers, but the layoffs are happening anyway. (The Atlantic)  
+ Software giant Atlassian is slashing 10% of its workforce ahead of an AI push. (The Guardian) 
+ At least lawyers’ jobs look safer than first feared. (MIT Technology Review) 
 
7 Software giants claim they’re not worried that AI will destroy them 
Oracle and Salesforce CEOs have dismissed fears of an “SaaS-pocalypse.” (Reuters) 
 
8 Lab-grown brains have started solving engineering problems 
Scientists trained the organoid to decode an engineering task. (Popular Mechanics) 
+ Other organoids are being impregnated with human embryos. (MIT Technology Review) 
 
9 English-language music is losing its grip on Spotify 
The variety of languages in its top 50 songs has doubled since 2020. (BBC) 
 
10 AI is redrawing the boundaries of physics 
It’s blurring the boundaries between a machine and a researcher. (The Economist $)  

Quote of the day 

“Elon Musk is an aggressive and irresponsible salesman, who has a long history of making dangerous design choices and over-promising the features of his products.”

—A lawsuit over Tesla’s Full Self-Driving mode takes aim at the company’s CEO, Gizmodo reports.

One More Thing

This town’s mining battle reveals the contentious path to a cleaner future 

In a tiny Minnesota town, an exploratory mining company called Talon plans to dig up as much as 725,000 metric tons of raw ore per year. 

It says the site will help power a greener future for the US by producing the nickel needed for EV batteries. But many local citizens aren’t eager for major mining operations near their towns.  

The tensions have created a test case for conflicts between local environmental concerns and global climate goals. Read the full story. 

—James Temple 

We can still have nice things 

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Mario is finally getting a LEGO minifigure.  
+ This new social platform boldly aims to burst filter bubbles. 
+ NASA is backing DSLR cameras by taking a trusty old Nikon D5 to the moon. 
+ This nuclear escalation simulator helped me learn to stop worrying and love the bomb. 

Just a few years ago, the battery industry was hot, hot, hot. There was a seemingly infinite number of companies popping up, with shiny new chemistries and massive fundraising rounds. My biggest problem was sifting through the pile to pick the most exciting news to cover.

That tide has turned, and in 2026, what seems to be in unlimited supply isn’t battery success stories but stumbles or straight-up implosions. Companies are failing, investors are pulling back, and batteries, especially for EVs, aren’t looking so hot anymore. On Monday, Steve Levine at The Information (paywalled link) reported that 24M Technologies, a battery company founded in 2010, was shutting down and would auction off its property.

The company itself has been silent, but this is the latest in a string of bad signs, and it’s a big one—at one point 24M was worth over $1 billion, and the company’s innovations could have worked with existing technology. So where does that leave the battery industry?

Many buzzy battery startups in recent years have been trying to sell some new, innovative chemistry to compete with lithium-ion batteries, the status quo that powers phones, laptops, electric vehicles, and even grid storage arrays today. Think sodium-ion batteries and solid-state cells.

24M wasn’t trying to sell a departure from lithium-ion but improvements that could work with the tech. One of the company’s major innovations was its manufacturing process, which involved essentially smearing materials onto sheets of metal to form the electrodes, a simpler and potentially cheaper technique than the standard one. 

The layers in the company’s batteries were thicker, which cut down on some of the inactive materials in cells and improved the energy density. That allows more energy to be stored in a smaller package, boosting the range of EVs—the company famously had a goal of a 1,000-mile battery (about 1,600 kilometers).

We’re still thin on details of what exactly went down at 24M and what comes next for its tech. The company didn’t get back to my questions sent to the official press email, and nobody picked up the phone when I called. 24M cofounder and MIT professor Yet-Ming Chiang declined to speak on the record.

For those who have been closely following the battery industry, more bad news isn’t too surprising. It feels as if everyone is short on money these days, and as purse strings tighten, there’s less interest in novel ideas. “It just feels like there’s not a lot of appetite for innovation,” says Kara Rodby, a technical principal at Volta Energy Technologies, a venture capital firm that focuses on the energy storage industry.

Natron Energy, one of the leading sodium-ion startups in the US, shut down operations in September last year. Ample, an EV battery-swapping company, filed for bankruptcy in December 2025.  

There were always going to be failures from the recent battery boom. Money was flowing to all sorts of companies, some pitching truly wild ideas. But what recent months have made clear is that the battery market is turning brutal, even for the relatively safe bets.

Because 24M’s technology was designed to work into existing lithium-ion chemistry, it could have been an attractive candidate for existing battery companies to license or even acquire. “It’s a great example of something that should have been easier,” Rodby says.  

The gutting of major components of the Inflation Reduction Act, key legislation in the US that provided funding and incentives for batteries and EVs, certainly hasn’t helped. The EV market in the US is cooling off, with automakers canceling EV models and slashing factory plans.

There are bright spots. China’s battery industry is thriving, and its battery and EV giants are looking ever more dominant. The market for stationary energy storage is also still seeing positive signs of growth, even in the US. 

But overall, it’s not looking great. 

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

Feng Qingyang had always hoped to launch his own company, but he never thought this would be how—or that the day would come this fast. 

Feng, a 27-year-old software engineer based in Beijing, started tinkering with OpenClaw, a popular new open-source AI tool that can take over a device and autonomously complete tasks for a user, in January. He was immediately hooked, and before long he was helping other curious tech workers with less technical proficiency install the AI agent.

Feng soon realized this could be a lucrative opportunity. By the end of January, he had set up a page on Xianyu, a secondhand shopping site, advertising “OpenClaw installation support.” “No need to know coding or complex terms. Fully remote,” reads the posting. “Anyone can quickly own an AI assistant, available within 30 minutes.” 

At the same time, the broader Chinese public was beginning to catch on—and the tool, which had begun as a niche interest among tech workers, started to evolve into a popular sensation.

Feng quickly became inundated with requests, and he started chatting with customers and managing orders late into the night. At the end of February, he quit his job. His side gig has now grown into a full-fledged professional operation with over 100 employees. So far, the store has handled 7,000 orders, each worth about 248 RMB or approximately $34. 

“Opportunities are always fleeting,” says Feng. “As programmers, we are the first to feel the winds shift.”

Feng is among a small cohort of savvy early adopters turning China’s OpenClaw craze into cash. As users with little technical background want in, a cottage industry of people offering installation services and preconfigured hardware has sprung up to meet them. The sudden rise of these tinkerers and impromptu consultants shows just how eager the general public in China is to adopt cutting-edge AI—even when there are huge security risks. 

A “lobster craze”

“Have you raised a lobster yet?” 

Xie Manrui, a 36-year-old software engineer in Shenzhen, says he has heard this question nonstop over the past month. “Lobster” is the nickname Chinese users have given to OpenClaw—a reference to its logo.

Xie, like Feng, has been experimenting with OpenClaw since January. He’s built new open-source tools on top of the ecosystem, including one that visualizes the agent’s progress as an animated little desktop worker and another that lets users voice-chat with it. 

“I’ve met so many new people through ‘lobster raising,’” says Xie. “Many are lawyers or doctors, with little technical background, but all dedicated to learning new things.”

Lobsters are indeed popping up everywhere in China right now—on and offline. In February, for instance, the entrepreneur and tech influencer Fu Sheng hosted a livestream showing off OpenClaw’s capabilities that got 20,000 views. And just last weekend, Xie attended three different OpenClaw events in Shenzhen, each drawing more than 500 people. These self-organized, unofficial gatherings feature power users, influencers, and sometimes venture capitalists as speakers. The biggest event Xie attended, on March 7, drew more than 1,000 people; in the packed venue, he says, people were shoulder to shoulder, with many attendees unable to even get a seat.

Now China’s AI giants are starting to piggyback on the trend too, promoting their models, APIs,  and cloud services (which can be used with OpenClaw), as well as their own OpenClaw-like agents. Earlier this month, Tencent held a public event offering free installation support for OpenClaw, drawing long lines of people waiting for help, including elderly users and children.

This sudden burst in popularity has even prompted local governments to get involved. Earlier this month the government of Longgang, a district in Shenzhen, released several policies to support OpenClaw-related ventures, including free computing credits and cash rewards for standout projects. Other cities, including Wuxi, have begun rolling out similar measures.

These policies only catalyze what’s already in the air. “It was not until my father, who is 77, asked me to help install a ‘lobster’ for him that I realized this thing is truly viral,” says Henry Li, a software engineer based in Beijing. 

A programmer gold rush

What’s making this moment particularly lucrative for people with technical skills, like Feng, is that so many people want OpenClaw, but not nearly as many have the capabilities to access it. Setting it up requires a level of technical knowledge most people do not possess, from typing commands into a black terminal window to navigating unfamiliar developer platforms. On the hardware side, an older or budget laptop may struggle to run it smoothly. And if the tool is not installed on a device separate from someone’s everyday computer, or if the data accessible to OpenClaw is not properly partitioned, the user’s privacy could be at risk—opening the door to data leaks and even malicious attacks. 

Chris Zhao, known as “Qi Shifu” online, organizes OpenClaw social media groups and events in Beijing. On apps like Rednote and Jike, Zhao routinely shares his thoughts on AI, and he asks other interested users to leave their WeChat ID so he can invite them to a semi-private group chat. The proof required to join is a screenshot that shows your “lobster” up and running. Zhao says that even in group chats for experienced users, hardware and cloud setup remain a constant topic of discussion.

The relatively high bar for setting up OpenClaw has generated a sense of exclusivity, creating a natural opening for a service industry to start unfolding around it. On Chinese e-commerce platforms like Taobao and JD, a simple search for “OpenClaw” now returns hundreds of listings, most of them installation guides and technical support packages aimed at nontechnical users, priced anywhere from 100 to 700 RMB (approximately $15 to $100). At the higher end, many vendors offer to come to help you in person. 

Like Feng, most providers of these services are early adopters with some technical ability who are looking for a side gig. But as demand has surged, some have found themselves overwhelmed. Xie, the developer in Shenzhen who created tools to layer on OpenClaw, was asked by a friend who runs one such business to help out over the weekend; the friend had a customer who worked in e-commerce and had little technical experience, so Xie had to show up in person to get it done. He walked away with 600 RMB ($87) for the afternoon.

The growing demand has also pushed vendors like Feng to expand quickly. He has now standardized his operation into tiers: a basic installation, a custom package where users can make specific requests like configuring a preferred chat app, and an ongoing tutoring service for those who want a hand to hold as they find their footing with the technology.

Other vendors in China are making money combining OpenClaw with hardware. Li Gong, a Shenzhen-based seller of refurbished Mac computers, was among the first online sellers to do this—offering Mac minis and MacBooks with OpenClaw preinstalled. Because OpenClaw is designed to operate with deep access to a hard drive and can run continuously in the background unattended, many users prefer to install it on a separate device rather than on the one they use every day. This would help prevent bad actors from infiltrating the program and immediately gaining access to a wide swathe of someone’s personal information. Many turn to secondhand or refurbished options to keep the cost down. Li says that in the last two weeks, orders have increased eightfold.

Though OpenClaw itself is a new technology, the general practice of buying software bundles, downloading third-party packages, and seeking out modified devices is nothing new for many Chinese internet users, says Tianyu Fang, a PhD candidate studying the history of technology at Harvard University. Many users pay for one-off IT support services for tasks from installing Adobe software to jailbreaking a Kindle.

Still, not everyone is getting swept up. Jiang Yunhui, a tech worker based in Ningbo, worries that ordinary users who struggle with setup may not be the right audience for a technology that is still effectively in testing. 

“The hype in first-tier cities can be a little overblown,” he says. “The agent is still a proof of concept, and I doubt it would be of any life-changing use to the average person for now.” He argues that using it safely and getting anything meaningful out of it requires a level of technical fluency and independent judgment that most new users simply don’t have yet.

He’s not alone in his concerns. On March 10, the Chinese cybersecurity regulator CNCERT issued a warning about the security and data risks tied to OpenClaw, saying it heightens users’ exposure to data breaches.

Despite the potential pitfalls, though, China’s enthusiasm for OpenClaw doesn’t seem to be slowing.

Feng, now flush with the earnings from his operation, wants to use the momentum—and the capital—to keep building out his own venture with AI tools at the center of it.

“With OpenClaw and other AI agents, I want to see if I can run a one-person company,” he says. “I’m giving myself one year.”

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How Pokémon Go is giving delivery robots an inch-perfect view of the world 

Pokémon Go was the world’s first augmented-reality megahit. Released in 2016 by Niantic, the AR twist on the juggernaut Pokémon franchise fast became a global phenomenon. “500 million people installed that app in 60 days,” says Brian McClendon, CTO at Niantic Spatial, an AI company that Niantic spun out last year.  

Now Niantic Spatial is using that vast trove of crowdsourced data to build a kind of world model—a buzzy new technology that grounds the smarts of LLMs in real environments. The firm wants to use it to help robots navigate more precisely. Read the full story. 

—Will Douglas Heaven 

MIT Technology Review Narrated: America was winning the race to find Martian life. Then China jumped in. 

In July 2024, after more than three years on Mars, the Perseverance rover came across a peculiar rocky outcrop. Instead of the usual crystals or sedimentary layers, this one had spots. Those specks were the best hint yet of alien life.  

NASA began a new mission to bring the rocks back to Earth to study. But now, just over a year and a half later, the project is on life support. As a result, those oh-so-promising rocks may be stuck out there forever. 

This also means that, in the race to find evidence of alien life, America has effectively ceded its pole position to its greatest geopolitical rival: China. The superpower is moving full steam ahead with its own version of NASA’s mission.  

—Robin George Andrews 

This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. 

The must-reads 

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 

1 Viral AI fakes of the Iran war are flooding X 
And Grok is failing to flag them. (Wired $) 
+ The conflict could wreak havoc on data centers and electricity costs. (The Verge)  
+ Pro-Iran bots are weaponizing posts about Epstein. (Gizmodo)  
+ AI is turning the Iran conflict into a show. (MIT Technology Review) 

2 Anthropic fears the loss of billions due to the Pentagon’s blacklisting  
That’s what the company has told a judge as it seeks to block its designation as a supply-chain risk. (Bloomberg $) 
+ Microsoft has backed the company in its legal fight with the Pentagon. (FT $) 
+ OpenAI’s “compromise” with the DoD dealt a big blow to Anthropic. (MIT Technology Review) 
 
3 Meta has bought a social network that’s exclusively for bots 
Moltbook is a Reddit-like site where AI agents interact with each other. (NYT $) 
+ The platform is  AI theater. (MIT Technology Review)  
 
4 Ukraine is eagerly offering the US its expertise and tech to counter Iranian drones 
Kyiv has sent drones and UAV specialists to military bases in Jordan. (WSJ $) 
+ A radio-obsessed civilian is shaping Ukraine’s drone defense. (MIT Technology Review) 
 
5 OnlyFans “chatters” are earning $2 per hour to impersonate models 
A worker in the Philippines described the job as “heartbreaking” and “icky.” (BBC) 
 
6 The DHS has removed officials who objected to “illegal” orders about surveillance tech 
The officers had refused to mislabel records about the technologies in order to block their release. (Wired) 

7 This startup is building data centers run on brain cells  
The “biological data centers” are coming to Melbourne and Singapore. (New Scientist $) 

8 Anduril is expanding into space defense 
The company is buying ExoAnalytic, which specializes in missile defense tracking. (Reuters) 
+ We saw a demo of an AI system powering Anduril’s vision for war. (MIT Technology Review) 
 
9 Big tech has a new big idea: AI compute as compensation 
Silicon Valley is pitching it as a job perk. (Business Insider) 
 
10 Wordle’s creator is back with a new game 
It’s inspired by cryptic crosswords. (The New Yorker $)  

Quote of the day 

“You come for the Epstein content, and you stay for the propaganda.” 

—Bret Schafer, an expert on information manipulation, tells the Washington Post how pro-Iran networks are gaining traction with posts about Epstein. 

One More Thing 

The quest to figure out farming on Mars  

If ever a blade of grass grew on Mars, those days are over. But could they begin again? What would it take to grow plants to feed future astronauts on Mars?  

To grow food there, we can’t just drop seeds in the ground and add water. We will need to create a layer of soil that can support life. And to do that, we first have to get rid of the red planet’s toxic salts.  

Researchers recently discovered a potential solution—and the early signs are promising. Read the full story.

We can still have nice things 

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line) 

+ Finally, a rebellion arises against mint’s tyranny over our teeth: Peanut Butter Cup toothpaste. 
+ DIY decorators rejoice! The humble paint tray has received an ingeniously simple renovation. 
+ Saudi surgeons have successfully separated two conjoined twins. 
+ If you’re looking for real innovation, check out British Pie Week’s beef rendang, jerk chicken, and double-size pasties. 

arXiv:2603.07728v1 Announce Type: new Abstract: Large language models (LLMs) such as GPT and Gemini have demonstrated remarkable capabilities in contextual understanding and reasoning. The strong performance of LLMs has sparked growing interest in leveraging them to automate tasks traditionally dependent on human expertise. Recently, LLMs have been integrated into intelligent agents capable of operating structural analysis software (e.g., OpenSees) to construct structural models and perform analyses. However, existing LLMs are limited in handling multi-step structural modeling due to frequent hallucinations and error accumulation during long-sequence operations. To this end, this study presents a novel multi-agent architecture to automate the structural modeling and analysis using OpenSeesPy. First, problem analysis and construction planning agents extract key parameters from user descriptions and formulate a stepwise modeling plan. Node and element agents then operate in parallel to assemble the frame geometry, followed by a load assignment agent. The resulting geometric and load information is translated into executable OpenSeesPy scripts by code translation agents. The proposed architecture is evaluated on a benchmark of 20 frame problems over ten repeated trials, achieving 100% accuracy in 18 cases and 90% in the remaining two. The architecture also significantly improves computational efficiency and demonstrates scalability to larger structural systems.

arXiv:2603.07733v1 Announce Type: new Abstract: This work investigated the capabilities of different models, including the Llama-3 series of models and CHATGPT, with different forms of expression in solving discrete optimization problems by testing natural language datasets. In contrast to formal datasets with a limited scope of parameters, our dataset included a variety of problem types in discrete optimization problems and featured a wide range of parameter magnitudes, including instances with large parameter sets, integrated with augmented data. It aimed to (1) provide an overview of LLMs' ability in large-scale problems, (2) offer suggestions to those who want to solve discrete optimization problems automatically, and (3) regard the performance as a benchmark for future research. These datasets included original, expanded and augmented datasets. Among these three datasets, the original and augmented ones aimed for evaluation while the expanded one may help finetune a new model. In the experiment, comparisons were made between strong and week models, CoT methods and No-CoT methods on various datasets. The result showed that stronger model performed better reasonably. Contrary to general agreement, it also showed that CoT technique was not always effective regarding the capability of models and disordered datasets improved performance of models on easy to-understand problems, even though they were sometimes with high variance, a manifestation of instability. Therefore, for those who seek to enhance the automatic resolution of discrete optimization problems, it is recommended to consult the results, including the line charts presented in the Appendix, as well as the conclusions drawn in this study for relevant suggestions.

arXiv:2603.07890v1 Announce Type: new Abstract: We propose image segmentation as a visual diagnostic testbed for coalition formation in hedonic games. Modeling pixels as agents on a graph, we study how a granularization parameter shapes equilibrium fragmentation and boundary structure. On the Weizmann single-object benchmark, we relate multi-coalition equilibria to binary protocols by measuring whether the converged coalitions overlap with a foreground ground-truth. We observe transitions from cohesive to fragmented yet recoverable equilibria, and finally to intrinsic failure under excessive fragmentation. Our core contribution links multi-agent systems with image segmentation by quantifying the impact of mechanism design parameters on equilibrium structures.

arXiv:2603.07972v1 Announce Type: new Abstract: While scaling individual Large Language Models (LLMs) has delivered remarkable progress, the next frontier lies in scaling collaboration through multi-agent systems (MAS). However, purely autonomous MAS remain ''closed-world'' systems, constrained by the static knowledge horizon of pre-trained models. This limitation makes them brittle on tasks requiring knowledge beyond training data, often leading to collective failure under novel challenges. To address this, we propose the Human-In-the-Loop Multi-Agent Collaboration (HILA) framework, a principled paradigm for human--agent collaboration. HILA trains agents to learn a metacognitive policy that governs when to solve problems autonomously and when to defer to a human expert. To operationalize this policy, we introduce Dual-Loop Policy Optimization, which disentangles immediate decision-making from long-term capability growth. The inner loop applies Group Relative Policy Optimization (GRPO) with a cost-aware reward to optimize deferral decisions, while the outer loop implements continual learning, transforming expert feedback into high-quality supervised signals that strengthen the agent's reasoning ability. Experiments on challenging mathematical and problem-solving benchmarks show that HILA, equipped with Dual-Loop Policy Optimization, consistently outperforms advanced MAS, establishing a principled foundation for collaborative and continually improving agentic systems.

arXiv:2603.08035v1 Announce Type: new Abstract: Reward modeling is essential for aligning Large Language Models(LLMs) with human preferences, yet conventional reward models suffer from poor interpretability and heavy reliance on costly expert annotations. While recent rubric-based approaches enhance evaluation transparency, they lack systematic quality control, yielding noisy and redundant criteria, failing to mitigate persistent biases (e.g., verbosity, position) in LLM evaluators, and creating a scalability-reliability trade-off. To address these limitations, we propose CDRRM (Contrast-Driven Rubric Reward Model), a framework built on a novel Contrast-then-Synthesis paradigm for high-quality rubric generation and guided preference judgment. CDRRM first conducts multi-dimensional contrastive profiling on preference pairs to identify causal discriminative factors, then synthesizes these insights into compact, context-aware rubrics to guide preference judg- ments. Extensive experiments on three authoritative benchmarks (RewardBench, RMBench, RMB) demonstrate that CDRRM achieves state-of-the-art performance across diverse domains and effectively mitigates aforementioned evaluation biases. Notably, our approach delivers exceptional data efficiency: training the rubric generator on only 3k high-quality samples empowers a frozen pre-trained judge model to outperform fully fine-tuned baselines. This work offers a scalable, interpretable, and data-efficient path for reward modeling.

The multi-year deal involves at least a gigawatt of compute power and also includes a strategic investment from Nvidia.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How AI is turning the Iran conflict into theater 

Much of the spotlight on AI in the Iran conflict has focused on models like Claude helping the US military decide where to strike. But a wave of “vibe-coded” intelligence dashboards—and the ecosystem surrounding them—reflect a new role that AI is playing in wartime: mediating information, often for the worse. 

These sorts of intelligence tools have much promise. Yet there are real reasons to be suspicious of their data feeds. Read the full story. 

—James O’Donnell 

This story is from The Algorithm, our weekly newsletter on AI. Sign up to receive it in your inbox every Monday. 

The must-reads 

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 

1 Anthropic has sued the US government  
The AI firm wants to stop the Pentagon from blacklisting it. (Reuters) 
+ The White House is preparing a new executive order to weed out the company’s technology. (Axios) 
+ Defense experts are alarmed. (CNBC) 
+.Google and OpenAI staff have filed a legal brief backing Anthropic against Trump. (Wired $) 
+ The company’s stance won many supporters. (MIT Technology Review) 

2 GPS jamming has become a crucial battleground in the Middle East  
The interference is endangering—and protecting—ships and planes. (BBC) 
+ Signal jamming has made navigating the Strait of Hormuz even more difficult. (Bloomberg) 
+ Quantum navigation offers a potential solution. (MIT Technology Review)  

3 A tech journalist found his AI clone editing for Grammarly 
It’s providing AI-generated feedback “inspired by” real writers without their consent. (Platformer) 
+ Could ChatGPT do the jobs of journalists and copywriters? (MIT Technology Review) 

4 Nvidia plans to launch an open-source platform for AI agents  
It’s already pitching the “NemoClaw” product to enterprise software firms. (Wired $) 
+ But don’t let the AI agents hype get ahead of reality (MIT Technology Review) 
 
5 A startup wants to launch a space mirror that reflects sunlight onto Earth 
Reflect Orbital reckons it could power solar panels at night. Scientists are appalled. (NYT) 

6 Yann LeCun’s AI startup has raised over $1bn in Europe’s largest seed round  
Meta’s former chief AI scientist plans to build systems that “understand the world.” (Bloomberg) 

7 Hinge’s CEO insists the app doesn’t rate users’ attractiveness 
Jackie Jantos’ strategy has helped Hinge defy the decline in dating apps. (FT $) 
+ AI companions are stealing hearts—and it’s getting weird. (New Yorker $) 
+ It’s surprisingly easy to fall into a relationship with a chatbot. (MIT Technology Review) 

8 “AI psychosis” could be afflicting your loved ones  
If so, here’s how you can help them. (404 Media) 
+ One solution: AI should be able to “hang up” on you. (MIT Technology Review) 

9 Nintendo is suing Trump over illegal tariffs 
The gaming giant has joined a lawsuit seeking over $200 billion in refunds. (Ars Technica) 

10 Bio-tech is turning ancient poop into a map of lost civilizations  
Molecular sensors are finding human traces where physical ruins have vanished. (Nature)    

Quote of the day 

“I don’t think any of us, whether it’s me or Dario [Amodei], Sam Altman, or Elon Musk, has any legitimacy to decide for society what is a good or bad use of AI.”

—Yann LeCun gives Wired his take on the Anthropic’s spat the Pentagon. 

One More Thing 

This giant microwave may change the future of war 

armed forces are hunting for a weapon that disables drones en masse—and they want it fast.  

One solution focuses on microwaves: high-powered electronic devices that push out kilowatts of power to zap the circuits of a drone as if it were the tinfoil you forgot to take off your leftovers when you heated them up. 

Defense tech startup Epirus may have the winning formula. The company has developed a cutting-edge, cost-efficient drone zapper that’s sparking the interest of the US military. And drones are just one of its targets. Read the full story. 

—Sam Dean 

We can still have nice things 

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.) 

+ Werner Herzog’s magnificent movie about Africa’s ghost elephants has arrived on Disney+ and Hulu. 
+ A “city killer” asteroid won’t hit Earth after all. Phew.  
+ The Met is publishing high-definition 3D scans of over 100 iconic works. 
+ Marty and Doc from Back to the Future are still BFFs in real life. 

Top image credit: MIT TECHNOLOGY REVIEW (ILLUSTRATION) | PHOTO OF MISSILE (US NAVY), AI-GENERATED IMAGE OF RUBBLE VIA X, SCREENSHOTS VIA WORLDMONITOR, GLOBALTHREATMAP 

Send asteroids to hi@technologyreview.com.  

You can follow me on LinkedIn. Thanks for reading! 
 
 

—Thomas  

arXiv:2603.08322v1 Announce Type: new Abstract: We study mathematical discovery through the lens of neurosymbolic reasoning, where an AI agent powered by a large language model (LLM), coupled with symbolic computation tools, and human strategic direction, jointly produced a new result in combinatorial design theory. The main result of this human-AI collaboration is a tight lower bound on the imbalance of Latin squares for the notoriously difficult case $n \equiv 1 \pmod{3}$. We reconstruct the discovery process from detailed interaction logs spanning multiple sessions over several days and identify the distinct cognitive contributions of each component. The AI agent proved effective at uncovering hidden structure and generating hypotheses. The symbolic component consists of computer algebra, constraint solvers, and simulated annealing, which provides rigorous verification and exhaustive enumeration. Human steering supplied the critical research pivot that transformed a dead end into a productive inquiry. Our analysis reveals that multi-model deliberation among frontier LLMs proved reliable for criticism and error detection but unreliable for constructive claims. The resulting human-AI mathematical contribution, a tight lower bound of $4n(n{-}1)/9$, is achieved via a novel class of near-perfect permutations. The bound was formally verified in Lean 4. Our experiments show that neurosymbolic systems can indeed produce genuine discoveries in pure mathematics.

arXiv:2603.08388v1 Announce Type: new Abstract: We propose a Hierarchical Error-Corrective Graph FrameworkforAutonomousAgentswithLLM-BasedActionGeneration(HECG),whichincorporates three core innovations: (1) Multi-Dimensional Transferable Strategy (MDTS): by integrating task quality metrics (Q), confidence/cost metrics (C), reward metrics (R), and LLM-based semantic reasoning scores (LLM-Score), MDTS achieves multi-dimensional alignment between quantitative performance and semantic context, enabling more precise selection of high-quality candidate strate gies and effectively reducing the risk of negative transfer. (2) Error Matrix Classification (EMC): unlike simple confusion matrices or overall performance metrics, EMC provides structured attribution of task failures by categorizing errors into ten types, such as Strategy Errors (Strategy Whe) and Script Parsing Errors (Script-Parsing-Error), and decomposing them according to severity, typical actions, error descriptions, and recoverability. This allows precise analysis of the root causes of task failures, offering clear guidance for subsequent error correction and strategy optimization rather than relying solely on overall success rates or single performance metrics. (3) Causal-Context Graph Retrieval (CCGR): to enhance agent retrieval capabilities in dynamic task environments, we construct graphs from historical states, actions, and event sequences, where nodes store executed actions, next-step actions, execution states, transferable strategies, and other relevant information, and edges represent causal dependencies such as preconditions for transitions between nodes. CCGR identifies subgraphs most relevant to the current task context, effectively capturing structural relationships beyond vector similarity, allowing agents to fully leverage contextual information, accelerate strategy adaptation, and improve execution reliability in complex, multi-step tasks.

arXiv:2603.06585v1 Announce Type: cross Abstract: We present an open, sport-agnostic platform that turns tracking into comparable spatial measures across professional Ultimate, basketball, and soccer. Coaches in all three sports ask the same question: where is the usable space, and when should an off-ball run start? Our workflow standardizes inputs, provides timing-aware spatial evaluations, and makes it possible to reuse the same analysis across sports. We illustrate the approach with Ultimate as a focused testbed and then examine transfer between basketball and soccer. Together, these results show a practical path toward consistent, comparable evaluation across various invasion sports.

arXiv:2603.06590v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC) is designed to assess generalization beyond pattern matching, requiring models to infer symbolic rules from very few examples. In this work, we present a transformer-based system that advances ARC performance by combining neural inference with structure-aware priors and online task adaptation. Our approach is built on four key ideas. First, we reformulate ARC reasoning as a sequence modeling problem using a compact task encoding with only 125 tokens, enabling efficient long-context processing with a modified LongT5 architecture. Second, we introduce a principled augmentation framework based on group symmetries, grid traversals, and automata perturbations, enforcing invariance to representation changes. Third, we apply test-time training (TTT) with lightweight LoRA adaptation, allowing the model to specialize to each unseen task by learning its transformation logic from demonstrations. Fourth, we design a symmetry-aware decoding and scoring pipeline that aggregates likelihoods across augmented task views, effectively performing ``multi-perspective reasoning'' over candidate solutions. We demonstrate that these components work synergistically: augmentations expand hypothesis space, TTT sharpens local reasoning, and symmetry-based scoring improves solution consistency. Our final system achieves a significant improvement over transformer baselines and surpasses prior neural ARC solvers, closing the gap toward human-level generalization.

arXiv:2603.06599v1 Announce Type: cross Abstract: Artificial intelligence pipelines -- spanning data collection, model training, deployment, and post-deployment monitoring -- concentrate ethical risks that intensify with multimodal and agentic systems. Existing governance instruments, including the EU AI Act, the IEEE 7000 series, and the NIST AI Risk Management Framework, provide high-level guidance but often lack enforceable, end-to-end operational controls. This paper presents an ethics-by-design control architecture that embeds consequentialist, deontological, and virtue-ethical reasoning into stage-specific enforcement mechanisms across the AI lifecycle. The framework implements a triple-gate structure at each lifecycle stage: Metric gates (quantitative performance and safety thresholds), Governance gates (legal, rights, and procedural compliance), and Eco gates (carbon and water budgets and sustainability constraints). It specifies measurable trigger conditions, escalation paths, audit artefacts, and mappings to EU AI Act obligations and NIST RMF functions, enabling integration with existing MLOps and CI/CD pipelines. Illustrative examples from large language model pipelines demonstrate how gate-based controls can surface and constrain technical, social, and environmental risks prior to release and during runtime. The framework is accompanied by a preregistered evaluation protocol that defines ex ante success criteria and assessment procedures, enabling falsifiable evaluation of gate effectiveness. By translating normative commitments into enforceable and testable controls, the framework provides a practical basis for operational AI governance across organizational contexts, jurisdictions, and deployment scales.

arXiv:2603.06600v1 Announce Type: cross Abstract: Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates questions designed to deliberately induce incorrect responses from VLMs, thereby revealing their vulnerabilities. The core of this approach lies in fuzz testing and reinforcement finetuning: we transform a single input query into a large set of diverse variants through vision and language fuzzing. Based on the fuzzing outcomes, the question generator is further instructed by adversarial reinforcement fine-tuning to produce increasingly challenging queries that trigger model failures. With this approach, we can consistently drive down a target VLM's answer accuracy -- for example, the accuracy of Qwen2.5-VL-32B on our generated questions drops from 86.58\% to 65.53\% in four RL iterations. Moreover, a fuzzing policy trained against a single target VLM transfers to multiple other VLMs, producing challenging queries that degrade their performance as well.