WIRED put the latest consumer exoskeletons from Dnsys and Hypershell in a head-to-head test on a pro athletic track. Personal exoskeletons were everywhere at CES 2026 . There were ambitious designs from newcomers WiRobotics, Sumbu, Ascentiz, and Dephy, while Skip Mo/Go was back promoting its long-overdue tech trousers. Dnsys (pronounced Deen-sis), a comparatively well established name, had some new launches to tease, Hypershell was back with its top model, and Ascentiz had us sprinting across the show floor . An exoskeleton is a relatively new class of wearable device designed to enhance, support, or assist human movement, strength, posture, or even physical activity.

Super tough microbialites are some of the oldest evidence of life on Earth. Breakthroughs, discoveries, and DIY tips sent every weekday. Among the tricky carnivorous plants, great white shark-killing orca whales, and other remarkable flora and fauna that call South Africa home is a remarkable group of "living rocks." Called microbialites, these communities are similar to coral reefs and are built up by microbes. These tiny living organisms absorb and release dissolved minerals into more solid rock-like forms.

For the first argument, we use induction. For the second part, we it is essentially a Coupon Collector's problem. The colors represent the target environment. The environment is shown in Figure 6. The results are shown in Figure 5. Forward} to reach the target grid (green).

With healthy populations of animals that disperse seeds, tropical forests can absorb up to four times more carbon. Deforestation, hunting, and wildlife trade threaten the hornbill's ability to disperse seeds throughout Asian tropical forests. A lot of attention has been paid to how climate change can reduce biodiversity. Now MIT researchers have shown that the reverse is also true: Loss of biodiversity can jeopardize regrowth of tropical forests, one of Earth's most powerful tools for mitigating climate change. Combining data from thousands of previous studies and using new tools for quantifying interconnected ecological processes, the researchers analyzed numerous tropical sites where deforestation was being followed by natural regrowth, focusing on the role of animals such as birds and monkeys that spread plant seeds by eating them in one place and then defecating someplace else. Evan Fricke, a research scientist in the MIT Department of Civil and Environmental Engineering and the lead author of a paper on the work, has studied such animals for 15 years, showing that without their role, trees have lower survival rates and a harder time keeping up with environmental changes.

Allocating more computation during inference time (test-time scaling) improves language model performance, especially for reasoning tasks. To address this inefficiency, we introduce a general test-time calibration framework that adaptively modifies the model toward high-reward reasoning paths, with theoretical guarantees of improving the lower bound of expected reward under finite sampling, all without large language model (LLM) retraining. Within this framework, we propose CarBoN (Calibrated Best-of-N), a two-phase method that first explores the solution space and then learns a calibration of the logits via an input-specific temperature T and additive shift vector δ, guiding generation toward more reliable reasoning. Experiments on MA TH-500 and AIME-2024 show that CarBoN improves efficiency, with up to 4 fewer rollouts to reach the same accuracy, while often achieving higher accuracy under fixed budgets. We also analyze the complementary roles of T and δ in balancing output diversity and correctness, and demonstrate that the framework also generalizes to step-level sampling strategies such as beam search. Test-time scaling (TTS) is a practical alternative to ever-larger training, enabling models to "think longer" at inference by allocating additional computation to reasoning. As these studies suggest, TTS allows smaller LLMs to match or even outperform larger ones, providing a more cost-efficient and flexible inference strategy. Despite these benefits, simply increasing test-time compute does not guarantee optimal performance. Recent work has shown that inference without effective verification is often sub-optimal, as models may spend additional computation on low-quality reasoning paths (Setlur et al., 2025). To overcome this inefficiency, we propose a general test-time calibration framework that strategically reallocates the inference budget by leveraging feedback from a verifier or reward model during inference. Rather than treating generation as a fixed forward pass, the model adaptively steers toward high-reward (likely correct) regions, improving reasoning reliability under a fixed query budget. The reward is defined as the inverse distance to the target plus noise.

Analysis by researchers at the University of Bern suggests that water and other volatile compounds arrived on Earth from outer space--specifically via a collision with a Mars-sized planet billions of years ago. Many scientists believe that in its infancy, Earth collided with another world the size of Mars, and that instead of being destroyed, it was transformed, incorporating the mass of that foreign body to become the planet we know. Recent research adds another layer of relevance to that hypothesized cosmic event: Scientists believe that without that other body, the basic conditions for life to emerge on Earth might never have appeared. A team from the University of Bern in Switzerland argues that, due to its proximity to the sun, the proto-Earth that existed before this potential collision lost the volatile elements essential to form complex molecules. Any hydrogen, carbon, or sulfur, their analysis suggests, evaporated in just the first 3 million years after proto-Earth's formation.

For the first argument, we use induction. For the second part, we it is essentially a Coupon Collector's problem. The colors represent the target environment. The environment is shown in Figure 6. The results are shown in Figure 5. Forward} to reach the target grid (green).

Large Language Models are versatile, general-purpose tools with a wide range of applications. Recently, the advent of "reasoning models" has led to substantial improvements in their abilities in advanced problem-solving domains such as mathematics and software engineering. In this work, we assessed the ability of reasoning models to perform chemistry tasks directly, without any assistance from external tools. We created a novel benchmark, called ChemIQ, consisting of 816 questions assessing core concepts in organic chemistry, focused on molecular comprehension and chemical reasoning. Unlike previous benchmarks, which primarily use multiple choice formats, our approach requires models to construct short-answer responses, more closely reflecting real-world applications. The reasoning models, OpenAI's o3-mini, Google's Gemini Pro 2.5, and DeepSeek R1, answered 50%-57% of questions correctly in the highest reasoning modes, with higher reasoning levels significantly increasing performance on all tasks. These models substantially outperformed the non-reasoning models which achieved only 3%-7% accuracy. We found that Large Language Models can now convert SMILES strings to IUPAC names, a task earlier models were unable to perform. Additionally, we show that the latest reasoning models can elucidate structures from 1D and 2D 1H and 13C NMR data, with Gemini Pro 2.5 correctly generating SMILES strings for around 90% of molecules containing up to 10 heavy atoms, and in one case solving a structure comprising 25 heavy atoms. For each task, we found evidence that the reasoning process mirrors that of a human chemist. Our results demonstrate that the latest reasoning models can, in some cases, perform advanced chemical reasoning.

There are reasonable arguments to suggest that AI tools may eventually help reduce emissions, as the IEA report underscores. But what we know for sure is that they're driving up energy demand and emissions today--especially in the regional pockets where data centers are clustering. So far, these facilities, which generally run around the clock, are substantially powered through natural-gas turbines, which produce significant levels of planet-warming emissions. Electricity demands are rising so fast that developers are proposing to build new gas plants and convert retired coal plants to supply the buzzy industry. The other thing we know is that there are better, cleaner ways of powering these facilities already, including geothermal plants, nuclear reactors, hydroelectric power, and wind or solar projects coupled with significant amounts of battery storage. The trade-off is that these facilities may cost more to build or operate, or take longer to get up and running.

What's new: A company called Aspen Aerogels, which makes materials to go inside EVs' batteries to stop fires spreading, just got a 670.6 million loan commitment from the US Department of Energy. The company will use the money to finish building a new factory in Georgia to produce its materials. Why it matters: As more EVs hit the roads, concern is growing about the relatively rare but dangerous problem of battery fires. Materials like Aspen Aerogels' thermal barriers could help improve safety. MIT Technology Review Narrated: Inside the quest to engineer climate-saving "super trees" Biotech startup Living Carbon is trying to design trees that grow faster and grab more carbon than their natural peers, as well as trees that resist rot, keeping that carbon out of the atmosphere.