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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. An experimental surgery is helping cancer survivors give birth An experimental surgical procedure that’s helping people have babies after they’ve had  treatment for bowel or rectal cancer. Radiation and chemo can have pretty damaging side effects that mess up the uterus and ovaries. Surgeons are pioneering a potential solution: simply stitch those organs out of the way during cancer treatment. Once the treatment has finished, they can put the uterus—along with the ovaries and fallopian tubes—back into place. It seems to work! Last week, a team in Switzerland shared news that a baby boy had been born after his mother had the procedure. Baby Lucien was the fifth baby to be born after the surgery and the first in Europe, and since then at least three others have been born. Read the full story. —Jessica Hamzelou This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.  Bangladesh’s garment-making industry is getting greener Pollution from textile production—dyes, chemicals, and heavy metals—is common in the waters of the Buriganga River as it runs through Dhaka, Bangladesh. It’s among many harms posed by a garment sector that was once synonymous with tragedy: In 2013, the eight-story Rana Plaza factory building collapsed, killing 1,134 people and injuring some 2,500 others.  But things are starting to change. In recent years the country has become a leader in “frugal” factories that use a combination of resource-efficient technologies to cut waste, conserve water, and build resilience against climate impacts and global supply disruptions.  The hundreds of factories along the Buriganga’s banks and elsewhere in Bangladesh are starting to stitch together a new story, woven from greener threads. Read the full story. —Zakir Hossain Chowdhury This story is from the most recent print issue of MIT Technology Review magazine, which shines a light on the exciting innovations happening right now. If you haven’t already, subscribe now to receive future issues once they land. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 ICE used a private jet to deport Palestinian men to Tel Aviv The luxury aircraft belongs to Donald Trump’s business partner Gil Dezer. (The Guardian)+ Trump is mentioned thousands of times in the latest Epstein files. (NY Mag $) 2 How Jeffrey Epstein kept investing in Silicon ValleyHe continued to plough millions of dollars into tech ventures despite spending 13 months in jail. (NYT $)+ The range of Epstein’s social network was staggering. (FT $)+ Why was a picture of the Mona Lisa redacted in the Epstein files? (404 Media) 3 The risks posed by taking statins are lower than we realisedThe drugs don’t cause most of the side effects they’re blamed for. (STAT)+ Statins are a common scapegoat on social media. (Bloomberg $) 4 Russia is weaponizing the bitter winter weatherIt’s focused on attacking Ukraine’s power grid. (New Yorker $)+ How the grid can ride out winter storms. (MIT Technology Review) 5 China has a major spy-cam porn problemHotel guests are being livestreamed having sex to an online audience without their knowledge. (BBC) 6 Geopolitical gamblers are betting on the likelihood of warAnd prediction markets are happily taking their money. (Rest of World) 7 Oyster farmers aren’t signing up to programs to ease water pollutionThe once-promising projects appear to be fizzling out. (Undark)+ The humble sea creature could hold the key to restoring coastal waters. Developers hate it. (MIT Technology Review) 8 Your next payrise could be approved by AIMaybe your human bosses aren’t the ones you need to impress any more. (WP $) 9 The FDA has approved a brain stimulation device for treating depressionIt’s paving the way for a non-invasive, drug-free treatment for Americans. (IEEE Spectrum)+ Here’s how personalized brain stimulation could treat depression. (MIT Technology Review) 10 Cinema-goers have had enough of AIMovies focused on rogue AI are flopping at the box office. (Wired $)+ Meanwhile, Republicans are taking aim at “woke” Netflix. (The Verge) Quote of the day “I’m all for removing illegals, but snatching dudes off lawn mowers in Cali and leaving the truck and equipment just sitting there? Definitely not working smarter.”  —A web user in a forum for current and former ICE and border protection officers complains about the agency’s current direction, Wired reports. One more thing Is this the electric grid of the future? Lincoln Electric System, a publicly owned utility in Nebraska, is used to weathering severe blizzards. But what will happen soon—not only at Lincoln Electric but for all electric utilities—is a challenge of a different order. Utilities must keep the lights on in the face of more extreme and more frequent storms and fires, growing risks of cyberattacks and physical disruptions, and a wildly uncertain policy and regulatory landscape. They must keep prices low amid inflationary costs. And they must adapt to an epochal change in how the grid works, as the industry attempts to transition from power generated with fossil fuels to power generated from renewable sources like solar and wind. The electric grid is bracing for a near future characterized by disruption. And, in many ways, Lincoln Electric is an ideal lens through which to examine what’s coming. Read the full story. —Andrew Blum 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 or skeet ’em at me.) + Glamour puss alert—NYC’s bodega cats are gracing the hallowed pages of Vogue.+ Ancient Europe was host to mysterious hidden tunnels. But why?+ If you’re enjoying the new season of Industry, you’ll love this interview with the one and only Ken Leung.+ The giant elephant shrew is the true star of Philly Zoo.

How do you combine SigLIP2, DINOv3, and SAM3 into a single vision backbone without sacrificing dense or segmentation performance? NVIDIA’s C-RADIOv4 is a new agglomerative vision backbone that distills three strong teacher models, SigLIP2-g-384, DINOv3-7B, and SAM3, into a single student encoder. It extends the AM-RADIO and RADIOv2.5 line, keeping similar computational cost while improving dense prediction quality, resolution robustness, and drop-in compatibility with SAM3. The key idea is simple. Instead of choosing between a vision language model, a self supervised dense model, and a segmentation model, C-RADIOv4 tries to approximate all three at once with one backbone. https://www.arxiv.org/pdf/2601.17237 Agglomerative distillation in RADIO The RADIO family uses agglomerative distillation. A single ViT style student is trained to match both dense feature maps and summary tokens from several heterogeneous teachers. Earlier RADIO models combined DFN CLIP, DINOv2, and SAM. They already supported multi resolution training but showed ‘mode switching’, where the representation changed qualitatively as input resolution changed. Later work such as PHI-S, RADIOv2.5, and FeatSharp added better multi resolution distillation and regularization, but the teacher set was still limited. C-RADIOv4 upgrades the teachers: SigLIP2-g-384 for stronger image text alignment DINOv3-7B for high quality self supervised dense features SAM3 for segmentation oriented features and compatibility with the SAM3 decoder The student is trained so that its dense features match DINOv3 and SAM3, while its summary tokens match SigLIP2 and DINOv3. This gives one encoder that can support classification, retrieval, dense prediction, and segmentation. Stochastic multi resolution training C-RADIOv4 uses stochastic multi resolution training rather than a small fixed set of resolutions. Training samples input sizes from two partitions: Low resolution: {128, 192, 224, 256, 384, 432} High resolution: {512, 768, 1024, 1152} SigLIP2 operates natively at 384 pixels. Its features are upsampled by a factor of 3 using FeatSharp to align with 1152 pixel SAM3 features. SAM3 is trained with mosaic augmentation at 1152 × 1152. This design smooths the performance curve over resolution and improves low resolution behavior. For example, on ADE20k linear probing, C-RADIOv4-H reaches around: 55.20 mIoU at 512 px 57.02 mIoU at 1024 px 57.72 mIoU at 1536 px The scaling trend is close to DINOv3-7B while using roughly an order of magnitude fewer parameters. Removing teacher noise with shift equivariant losses and MESA Distilling from large vision models tends to copy their artifacts, not just their useful structure. SigLIP2 has border noise patterns, and ViTDet style models can show window boundary artifacts. Direct feature regression can force the student to reproduce those patterns. C-RADIOv4 introduces two shift equivariant mechanisms to suppress such noise: Shift equivariant dense loss: Each teacher and the student see independently shifted crops of an image. Before computing the squared error, features are aligned via a shift mapping and the loss only uses overlapping spatial positions. Because the student never sees the same absolute positions as the teacher, it cannot simply memorize position fixed noise and is forced to track input dependent structure instead. Shift equivariant MESA: C-RADIOv4 also uses MESA style regularization between the online network and an EMA copy. Here again, the student and its EMA see different crops, features are aligned by a shift, and the loss is applied after layer normalization. This encourages smooth loss landscapes and robustness, while being invariant to absolute position. In addition, training uses DAMP, which injects multiplicative noise into weights. This further improves robustness to corruptions and small distribution shifts. Balancing teachers with an angular dispersion aware summary loss The summary loss in previous RADIO models used cosine distance between student and teacher embeddings. Cosine distance removes magnitude but not directional dispersion on the sphere. Some teachers, such as SigLIP2, produce embeddings concentrated in a narrow cone, while DINOv3 variants produce more spread out embeddings. If raw cosine distance is used, teachers with wider angular dispersion contribute larger losses and dominate optimization. In practice, DINOv3 tended to overshadow SigLIP2 in the summary term. C-RADIOv4 replaces this with an angle normalized loss. The squared angle between student and teacher embeddings is divided by the teacher’s angular dispersion. Measured dispersions show SigLIP2-g-384 around 0.694, while DINOv3-H+ and DINOv3-7B are around 2.12 and 2.19. Normalizing by these values equalizes their influence and preserves both vision language and dense semantics. Performance: classification, dense prediction, and Probe3d On ImageNet-1k zero shot classification, C-RADIOv4-H reaches about 83.09 % top-1 accuracy. It matches or improves on RADIOv2.5-H and C-RADIOv3-H across resolutions, with the best performance near 1024 px. On k-NN classification, C-RADIOv4-H improves over RADIOv2.5 and C-RADIOv3, and matches or surpasses DINOv3 starting around 256 px. DINOv3 peaks near 192–256 px and then degrades, while C-RADIOv4 keeps stable or improving performance at higher resolutions. Dense and 3D aware metrics show the intended tradeoff. On ADE20k, PASCAL VOC, NAVI, and SPair, C-RADIOv4-H and the SO400M variant outperform earlier RADIO models and are competitive with DINOv3-7B on dense benchmarks. For C-RADIOv4-H, typical scores are: ADE20k: 55.20 mIoU VOC: 87.24 mIoU NAVI: 63.44 SPair: 60.57 https://www.arxiv.org/pdf/2601.17237 On Probe3d, which includes Depth Normals, Surface Normals, NAVI, and SPair, C-RADIOv4-H achieves the best NAVI and SPair scores in the RADIO family. Depth and Surface metrics are close to those of C-RADIOv3-H, with small differences in either direction, rather than a uniform improvement. Integration with SAM3 and ViTDet-mode deployment C-RADIOv4 is designed to be a drop in replacement for the Perception Encoder backbone in SAM3. The SAM3 decoder and memory components remain unchanged. A reference implementation is provided in a SAM3 fork. Qualitative examples show that segmentation behavior is preserved for both text prompts such as “shoe”, “helmet”, “bike”, “spectator” and box prompts, and in some reported cases C-RADIOv4 based SAM3 resolves failure cases from the original encoder. For deployment, C-RADIOv4 exposes a ViTDet-mode configuration. Most transformer blocks use windowed attention, while a few use global attention. Supported window sizes range from 6 × 6 to 32 × 32 tokens, subject to divisibility with patch size and image resolution. On an A100, the SO400M model with window size at most 12 is faster than the SAM3

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. Every time OpenAI, Google, or Anthropic drops a new frontier large language model, the AI community holds its breath. It doesn’t exhale until METR, an AI research nonprofit whose name stands for “Model Evaluation & Threat Research,” updates a now-iconic graph that has played a major role in the AI discourse since it was first released in March of last year. The graph suggests that certain AI capabilities are developing at an exponential rate, and more recent model releases have outperformed that already impressive trend. That was certainly the case for Claude Opus 4.5, the latest version of Anthropic’s most powerful model, which was released in late November. In December, METR announced that Opus 4.5 appeared to be capable of independently completing a task that would have taken a human about five hours—a vast improvement over what even the exponential trend would have predicted. One Anthropic safety researcher tweeted that he would change the direction of his research in light of those results; another employee at the company simply wrote, “mom come pick me up i’m scared.” Credit: METR.ORG But the truth is more complicated than those dramatic responses would suggest. For one thing, METR’s estimates of the abilities of specific models come with substantial error bars. As METR explicitly stated on X, Opus 4.5 might be able to regularly complete only tasks that take humans about two hours, or it might succeed on tasks that take humans as long as 20 hours. Given the uncertainties intrinsic to the method, it was impossible to know for sure.  “There are a bunch of ways that people are reading too much into the graph,” says Sydney Von Arx, a member of METR’s technical staff. More fundamentally, the METR plot does not measure AI abilities writ large, nor does it claim to. In order to build the graph, METR tests the models primarily on coding tasks, evaluating the difficulty of each by measuring or estimating how long it takes humans to complete it—a metric that not everyone accepts. Claude Opus 4.5 might be able to complete certain tasks that take humans five hours, but that doesn’t mean it’s anywhere close to replacing a human worker. METR was founded to assess the risks posed by frontier AI systems. Though it is best known for the exponential trend plot, it has also worked with AI companies to evaluate their systems in greater detail and published several other independent research projects, including a widely covered July 2025 study suggesting that AI coding assistants might actually be slowing software engineers down.  But the exponential plot has made METR’s reputation, and the organization appears to have a complicated relationship with that graph’s often breathless reception. In January, Thomas Kwa, one of the lead authors on the paper that introduced it, wrote a blog post responding to some criticisms and making clear its limitations, and METR is currently working on a more extensive FAQ document. But Kwa isn’t optimistic that these efforts will meaningfully shift the discourse. “I think the hype machine will basically, whatever we do, just strip out all the caveats,” he says. Nevertheless, the METR team does think that the plot has something meaningful to say about the trajectory of AI progress. “You should absolutely not tie your life to this graph,” says Von Arx. “But also,” she adds, “I bet that this trend is gonna hold.” Part of the trouble with the METR plot is that it’s quite a bit more complicated than it looks. The x-axis is simple enough: It tracks the date when each model was released. But the y-axis is where things get tricky. It records each model’s “time horizon,” an unusual metric that METR created—and that, according to Kwa and Von Arx, is frequently misunderstood. To understand exactly what model time horizons are, it helps to know all the work that METR put into calculating them. First, the METR team assembled a collection of tasks ranging from quick multiple-choice questions to detailed coding challenges—all of which were somehow relevant to software engineering. Then they had human coders attempt most of those tasks and evaluated how long it took them to finish. In this way, they assigned the tasks a human baseline time. Some tasks took the experts mere seconds, whereas others required several hours. When METR tested large language models on the task suite, they found that advanced models could complete the fast tasks with ease—but as the models attempted tasks that had taken humans more and more time to finish, their accuracy started to fall off. From a model’s performance, the researchers calculated the point on the time scale of human tasks at which the model would complete about 50% of the tasks successfully. That point is the model’s time horizon.  All that detail is in the blog post and the academic paper that METR released along with the original time horizon plot. But the METR plot is frequently passed around on social media without this context, and so the true meaning of the time horizon metric can get lost in the shuffle. One common misapprehension is that the numbers on the plot’s y-axis—around five hours for Claude Opus 4.5, for example—represent the length of time that the models can operate independently. They do not. They represent how long it takes humans to complete tasks that a model can successfully perform.  Kwa has seen this error so frequently that he made a point of correcting it at the very top of his recent blog post, and when asked what information he would add to the versions of the plot circulating online, he said he would include the word “human” whenever the task completion time was mentioned. As complex and widely misinterpreted as the time horizon concept might be, it does make some basic sense: A model with a one-hour time

Automatic speech recognition (ASR) is becoming a core building block for AI products, from meeting tools to voice agents. Mistral’s new Voxtral Transcribe 2 family targets this space with 2 models that split cleanly into batch and realtime use cases, while keeping cost, latency, and deployment constraints in focus. The release includes: Voxtral Mini Transcribe V2 for batch transcription with diarization. Voxtral Realtime (Voxtral Mini 4B Realtime 2602) for low-latency streaming transcription, released as open weights. Both models are designed for 13 languages: English, Chinese, Hindi, Spanish, Arabic, French, Portuguese, Russian, German, Japanese, Korean, Italian, and Dutch. Model family: batch and streaming, with clear roles Mistral positions Voxtral Transcribe 2 as ‘two next-generation speech-to-text models’ with state-of-the-art transcription quality, diarization, and ultra-low latency. Voxtral Mini Transcribe V2 is the batch model. It is optimized for transcription quality and diarization across domains and languages and exposed as an efficient audio input model in the Mistral API. Voxtral Realtime is the streaming model. It is built with a dedicated streaming architecture and is released as an open-weights model under Apache 2.0 on Hugging Face, with a recommended vLLM runtime. A key detail: speaker diarization is provided by Voxtral Mini Transcribe V2, not by Voxtral Realtime. Realtime focuses strictly on fast, accurate streaming transcription. Voxtral Realtime: 4B-parameter streaming ASR with configurable delay Voxtral Mini 4B Realtime 2602 is a 4B-parameter multilingual realtime speech-transcription model. It is among the first open-weights models to reach accuracy comparable to offline systems with a delay under 500 ms. Architecture: ≈3.4B-parameter language model. ≈0.6B-parameter audio encoder. The audio encoder is trained from scratch with causal attention. Both encoder and LM use sliding-window attention, enabling effectively “infinite” streaming. Latency vs accuracy is explicitly configurable: Transcription delay is tunable from 80 ms to 2.4 s via a transcription_delay_ms parameter. The Mistral describes latency as “configurable down to sub-200 ms” for live applications. At 480 ms delay, Realtime matches leading offline open-source transcription models and realtime APIs on benchmarks such as FLEURS and long-form English. At 2.4 s delay, Realtime matches Voxtral Mini Transcribe V2 on FLEURS, which is appropriate for subtitling tasks where slightly higher latency is acceptable. From a deployment standpoint: The model is released in BF16 and is designed for on-device or edge deployment. It can run in realtime on a single GPU with ≥16 GB memory, according to the vLLM serving instructions in the model card. The main control knob is the delay setting: Lower delays (≈80–200 ms) for interactive agents where responsiveness dominates. Around 480 ms as the recommended “sweet spot” between latency and accuracy. Higher delays (up to 2.4 s) when you need accuracy as close as possible to the batch model. Voxtral Mini Transcribe V2: batch ASR with diarization and context biasing Voxtral Mini Transcribe V2 is a closed-weights audio input model optimized only for transcription. It is exposed in the Mistral API as voxtral-mini-2602 at $0.003 per minute. On benchmarks and pricing: Around 4% word error rate (WER) on the FLEURS transcription benchmark, averaged over the top 10 languages. “Best price-performance of any transcription API” at $0.003/min. Outperforms GPT-4o mini Transcribe, Gemini 2.5 Flash, Assembly Universal, and Deepgram Nova on accuracy in their comparisons. Processes audio ≈3× faster than ElevenLabs’ Scribe v2 while matching quality at one-fifth the cost. Enterprise-oriented features are concentrated in this model: Speaker diarization Outputs speaker labels with precise start and end times. Designed for meetings, interviews, and multi-party calls. For overlapping speech, the model typically emits a single speaker label. Context biasing Accepts up to 100 words or phrases to bias transcription toward specific names or domain terms. Optimized for English, with experimental support for other languages. Word-level timestamps Per-word start and end timestamps for subtitles, alignment, and searchable audio workflows. Noise robustness Maintains accuracy in noisy environments such as factory floors, call centers, and field recordings. Longer audio support Handles up to 3 hours of audio in a single request. Language coverage mirrors Realtime: 13 languages, with Mistral noting that non-English performance “significantly outpaces competitors” in their evaluation. https://mistral.ai/news/voxtral-transcribe-2 APIs, tooling, and deployment options The integration paths are straightforward and differ slightly between the two models: Voxtral Mini Transcribe V2 Served via the Mistral audio transcription API (/v1/audio/transcriptions) as an efficient transcription-only service. Priced at $0.003/min. (Mistral AI) Available in Mistral Studio’s audio playground and in Le Chat for interactive testing. Voxtral Realtime Available via the Mistral API at $0.006/min. Released as open weights on Hugging Face (mistralai/Voxtral-Mini-4B-Realtime-2602) under Apache 2.0, with official vLLM Realtime support. The audio playground in Mistral Studio lets users: Upload up to 10 audio files (.mp3, .wav, .m4a, .flac, .ogg) up to 1 GB each. Toggle diarization, choose timestamp granularity, and configure context bias terms. Key Takeaways Two-model family with clear roles: Voxtral Mini Transcribe V2 targets batch transcription and diarization, while Voxtral Realtime targets low-latency streaming ASR, both across 13 languages. Realtime model- 4B parameters with tunable delay: Voxtral Realtime uses a 4B architecture (≈3.4B LM + ≈0.6B encoder) with sliding-window and causal attention, and supports configurable transcription delay from 80 ms to 2.4 s. Latency vs accuracy trade-off is explicit: Around 480 ms delay, Voxtral Realtime reaches accuracy comparable to strong offline and realtime systems, and at 2.4 s it matches Voxtral Mini Transcribe V2 on FLEURS. Batch model adds diarization and enterprise features: Voxtral Mini Transcribe V2 provides diarization, context biasing with up to 100 phrases, word-level timestamps, noise robustness, and supports up to 3 hours of audio per request at $0.003/min. Deployment- closed batch API, open realtime weights: Mini Transcribe V2 is served via Mistral’s audio transcription API and playground, while Voxtral Realtime is priced at $0.006/min and also available as Apache 2.0 open weights with official vLLM Realtime support. Check out the Technical details and Model Weights. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. The post Mistral AI Launches Voxtral Transcribe 2: Pairing Batch Diarization And