Committee

Friday, October 16, 2026 Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next. Join senior AI reporter James O’Donnell as he interviews King about his work, being named one of MIT Technology Review’s Innovators Under 35 and new ways of seeing biology. Going live on October 16th at 18:30 BST / 1:30pm EDT / 10:30am PDT Register now Speakers: James O’Donnell, AI Reporter, and Samuel King, Bioengineering PhD Candidate, Stanford University/Arc Institute Related Stories He asked an AI to design viruses … and it worked. AI-designed viruses are here and already killing bacteria

arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right entities and properties and chains their relations. Language models offer a natural-language alternative but answer largely from memory, which is least reliable for less prominent entities. We study agents that instead answer by exploring the graph, and argue that two obstacles limit them: the lack of training data recording how a solver explores, and interfaces that add large graph results directly to the model’s context. We test three hypotheses: that the difficulty of graph search can be controlled through the structure of a question rather than only through obscure entities or wording; that much of the failure on long-horizon search comes from how retrieved evidence is managed rather than from the model itself; and that, in a suitable environment, open-weight models can match commercial closed ones. We construct multi-hop questions on a frozen Wikidata snapshot by replacing named entities with nested conditions, checking after each expansion that the target remains unique and that every new condition is necessary. We release 10,235 solving traces over single-entity and multi-hop questions, together with the recursive language model (RLM) harness that produced them, in which models batch graph calls, keep results in persistent Python state and interpret selected evidence through sub-calls. On 100 questions, the harness improves both models we ran under both interfaces compared with direct tool calling over the same functions: gpt-6-luna rises from 49 to 61 correct answers, doubling its multi-hop accuracy, and Qwen3.8-27B, an open-weight model served on a single GPU, from 60 to 74.

arXiv:2610.07535v1 Announce Type: cross Abstract: Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model effects which may dominate engagement dynamics in real-world deployments. To show this, we simulate a heterogeneous social network powered by several different base models and show that the amount of engagement an agent receives depends more on its base model than on its assigned persona. The attraction or repulsion effects of a base model strengthen dramatically when more models are added in the mix, suggesting that networks dynamics may converge to base model effects at scale. To help explain this effect, we conduct a series of content-mediating analyses, showing the predictability of base models across contexts as well as the relationship between a model’s lexical patterns and an engagement-maximizing style. In light of recent developments in mass multi-agent interaction, this work underscores the relevance of heterogeneous compositions in driving the outcomes of those networks

arXiv:2610.04753v2 Announce Type: replace Abstract: Autoregressive generation in Large Language Models (LLMs) is constrained by the memory and computational demands of attention mechanisms. Sparse attention methods mitigate this cost by selecting only high-probability entries of the attention matrix. We observe that in many such methods, this renders the probability-value multiplication negligible, shifting the bottleneck to the query-key step. Key heads can therefore be reduced to accelerate inference, while retaining more value heads preserves capacity with limited additional decoding cost. We introduce Sparse Asymmetric Group-Query Attention (SAGA), which decouples key and value head counts to exploit this principle, and pair it with approximate top-N (Atop-N) attention, a simple sparse attention method designed to study the interaction between sparsity and head-count asymmetry. We formalize the benefits of this asymmetry theoretically and validate them empirically through latency measurements and quality evaluations on models up to 1.5B parameters. Together, SAGA and Atop-N achieve end-to-end decoding speedups exceeding $2times$ over our full-attention GQA baseline at long contexts. Models trained from scratch with SAGA nearly match the quality of comparable GQA variants on the evaluated benchmarks. To facilitate adoption, we introduce an efficient fine-tuning method that converts pretrained models to the SAGA architecture, enabling practitioners to benefit from our approach without costly retraining.

X-energy is developing small modular reactors to satisfy some of the planet’s most power-hungry users: industrial manufacturers that need high temperatures to produce billions of tons of concrete, plastics, fibers, and chemicals. Solar panels and wind turbines are great at producing electricity but what heavy industry also needs is raw heat—and lots of it. The manufacture of concrete, glass, and steel, as well as the production of many chemicals, plastics, and fertilizers, are still highly reliant on fossil fuels to produce that heat.  Traditional nuclear reactors simply don’t get hot enough. The cooling systems they use require liquid water to work, and even applying pressure to raise the water’s boiling point leaves their maximum operating temperatures hundreds of degrees cooler than needed.  X-energy’s proposed Xe-100 reactor will use helium gas as a coolant instead, allowing it to operate at much lower pressures and higher temperatures. The helium will heat water and create super-heated steam, which industry can use either directly or to generate 80 megawatts (MW) of electricity via a turbine, or a combination of the two. That’s about one-tenth of the capacity of most reactors operating today. In common with other small modular reactors (SMRs), the Xe-100 will use a modern uranium-based fuel called TRISO (short for tristructural-isotropic) that is extremely resistant to corrosion and melting. Compact TRISO “pebbles” will be added continuously to the top of the reactor, then flow slowly to the bottom, where they will either be recycled or removed as waste. The company says the reactor will be intrinsically safe in the event of a loss of power—if temperatures inside rise, the nuclear reaction naturally slows and stops. Key indicators Industry: Nuclear fission Founded: 2009 Headquarters: Rockville, Maryland, US Notable fact: The first helium-cooled reactor was proposed for the World War II Manhattan Project but was passed over in favor of a simpler and cheaper water-cooled design. Potential for impact Industrial heating currently accounts for 18 percent of annual global greenhouse-gas emissions, with about half of that coming from high-temperature processes that are difficult to electrify. Co-locating small nuclear reactors at large manufacturing facilities could shrink some of the biggest carbon footprints in the world.  The Xe-100 reactor could also be used by the fossil fuel industry, which today uses vast quantities of natural gas to generate the heat needed to recover oil from tar sands, and during oil refining.  The flexibility of the Xe-100 means that it can be used for traditional electricity generation. By scaling down in size and power from today’s gigawatt-scale fission plants, small reactors like the Xe-100 can serve more markets—even down to individual data centers. AI and data centers now represent the single largest component of new demand for electricity in the US, and their consumption is expected to double again by 2030. X-energy says that up to 12 of its reactors could be clustered together to provide 24/7 power on site, at a lower cost than a traditional nuclear power station and without the hassle of adding transmission infrastructure. Caveats  The technology behind helium-cooled fission may be well understood, but the only commercial-scale power generation of this type today is in China, where two reactors have struggled to operate on a continuous basis. X-energy says that its vertically integrated fuel supply should ensure reliable operation, with reactors having a lifetime of 60 years.  The economic case for SMRs is also largely unproven. Nuclear costs and timelines have a habit of ballooning, and the US Energy Information Administration calculates that electricity from SMRs will cost more than six times that from solar farms. While the current environment in the US for regulation and government funding favors nuclear, that could change over X-energy’s long development time frame.  TRISO reactors appear excellent for safety and nonproliferation (the tough spheres would be difficult to turn into weapons) but are less impressive when it comes to waste. The Xe-100 might produce 10 times the volume of spent nuclear fuel per unit of energy as existing reactors, although its lower-level waste will need less sophisticated storage. Next steps  The US Nuclear Regulatory Commission has issued X-energy’s subsidiary with a fuel fabrication license for TRISO-X pebbles, with its first factory due for completion in 2028. An Xe-100 to supply heat and power for a large chemical facility in Texas operated by Dow has also cleared its first regulatory hurdle, and could be operational by the early 2030s. In tandem, X-energy is working toward a 320 MW cluster of four Xe-100 reactors in Richland, Washington, in collaboration with Amazon, and a larger 6 GW fleet in the UK’s northeast. Both projects target electricity generation in the 2030s.

As the team at MIT Technology Review set out to choose companies for this year’s edition of our annual list of Climate Tech Companies to Watch, the challenge felt more daunting than any year since we started compiling the list in 2023.  This year is shaping up to be one of the hottest ever recorded. The world must begin to reduce greenhouse-gas emissions to address the climate crisis, which is already affecting communities around the globe. Wildfires, floods, and other climate-fueled disasters are killing thousands and costing billions of dollars. At the same time, political shifts and international conflict have stalled progress, particularly in the US.  Technology alone can’t solve climate change. But we believe innovations can help us make real changes and improve lives around the world as we try to address one of the most pressing challenges of our time.  In this project, we look for companies developing technologies that can help us face the climate crisis. Some are driving down the greenhouse-gas emissions fueling climate change; others are working to reduce the dangers that communities face from extreme heat, drought, or other shifts brought on by a warming planet. We focus on companies that have established a track record, whether through research findings, capital raised, or deployments.  When we set out to build our list, we start by soliciting nominations from our team of reporters and editors. We ask experts for suggestions and reach out to academics, researchers, investors, and analysts. Once we have a pool of nominees, we then consider each one, digging through literature, reports, and patents to vet both the technology and the business.  We judge each company not only on its own merits, but also on how it might fit into our list overall. We want the final slate of companies to represent a range of industries and include firms based around the globe that are at different stages of development. We also aim to strike a balance between highlighting companies previously included and introducing new names. Finally, we consider some of the most important trends and events of the year and look for nominees that reflect those developments. The dismembering of US federal regulations and cuts to financial government incentives for innovation means that the country is no longer a leader in climate tech. Like last year, the 2026 list is largely composed of companies that are based outside the US. China is an obvious hotspot for technological innovation across energy and climate. This year’s list includes two Chinese companies: WeLion is building semi-solid-state batteries for electric vehicles, and Envision Energy is installing huge amounts of wind power and energy storage in China and around the world.  Several sectors stood out this year as being of particular importance. One of the biggest stories of the year is the rising demand for electricity, driven in part by AI data centers.  Energy storage is a key piece of that puzzle because it can smooth out variations in the electricity supply as grids incorporate more renewable power. We’ve included several energy storage companies on this year’s list. Energy Dome is building long-duration energy storage systems on the grid that rely on compressed carbon dioxide. Moment Energy is a Canadian company working to repurpose EV batteries for grid-scale storage. And Form Energy, which we also included in the 2024 edition of this list, is producing iron-based batteries that can store energy for multiple days.  A wide range of technologies is represented here: everything from mobile flood barriers to next-generation nuclear reactors. Some of the awardees might be familiar from our past coverage; others are just emerging and may be less well known.  We believe we’ve put together a list filled with businesses that are rising to the challenge of this moment. Political will has weakened even as the damages wrought by climate change continue to mount. By helping us make progress on tackling the climate crisis in such difficult times, these companies point to a better way forward.