TECH

Windows 11 26H2 brings a hidden performance boost — and may slash RAM usage

Windows 11 26H2 quietly brings several performance improvements, including a Low Latency Profile that makes common tasks feel faster. Some users are also reporting lower RAM usage after the update, although Microsoft has not confirmed that 26H2 itself is responsible.

The latest Windows 11 26H2 update may look like a minor release on paper, but some users are reporting noticeably lower memory use and faster response times after installing it. The update brings together several performance improvements that Microsoft has gradually delivered through monthly patches, including a Low Latency Profile designed to make common Windows actions feel quicker.

Windows 11 versions 24H2, 25H2 and 26H2 share the same servicing branch, meaning many of 26H2’s changes had already been delivered before the update formally arrived. Microsoft describes 26H2 as an enablement package that turns on selected features and resets the operating system’s support lifecycle.

One of the most noticeable changes is the Low Latency Profile. When a user opens the Start menu, Search, Notification Center or an application, Windows temporarily pushes the processor close to its maximum clock speed for one to three seconds. The system completes the task quickly and then allows the CPU to return to an idle state—a technique Microsoft calls “race to sleep.”

Microsoft began rolling out the feature to Windows shell elements in June, then expanded it to app launches in August. Tests cited by Windows Latest showed faster launch times for applications including WhatsApp, Calculator and the Weather app. Because Microsoft used controlled feature rollouts, not every PC received the changes at the same time. The broader 26H2 release should make those optimizations available to more systems.

The more surprising change in 26H2 is its reported RAM use. Windows Latest said that a three-year-old laptop with 16GB of memory used about 7GB immediately after restarting on 26H2, compared with more than 10GB before the update. A Reddit user cited by the publication reported around 2GB less memory use after booting a PC with 48GB of RAM, while another user said usage on a 32GB system fell from 9.1GB on Windows 11 25H2 to 7.5GB on 26H2. An 8GB system reportedly showed no change, illustrating that results can vary considerably by hardware and configuration.

However, there is no confirmation that 26H2 itself is responsible for the lower memory consumption. Startup applications, cached data, application versions and browser tab management can all affect the figures, while Microsoft has not announced a specific RAM reduction as part of the update.

The RAM changes may nevertheless be part of a broader optimization effort. Microsoft has previously said it is working to make Windows 11 more responsive under heavy load and reduce its memory footprint. The company has identified memory management, memory compression, WinUI and WebView2 as areas for improvement, and has described better performance on systems with 8 GB of RAM as a priority.

Windows 11 has also received improvements to File Explorer, Search and WinUI throughout the year. Microsoft says File Explorer is now faster and more responsive after optimizations that reduce its reliance on preloading. Together with the Low Latency Profile and other changes, these smaller improvements could make the operating system feel faster without introducing a major new feature.

Windows 11’s Low Latency Profile is making the whole OS feel snappier...Windows 11 24H2, 25H2, and 26H2 share one servicing branch, and 26H2 is just an enablement package. Microsoft’s what’s new page says many of its features “have already been delivered through monthly servicing updates,” and that 26H2 “enables selected features and resets the support lifecycle.”

Low Latency Profile is one of those features. When you open the Start menu, Search, Notification Center, or an app, Windows pushes the CPU close to its maximum frequency for one to three seconds, finishes the work, and lets the processor drop back to idle sooner. It’s called “race to sleep,” and it targets the little pauses that make Windows feel slow even when the CPU isn’t busy.

Microsoft rolled it out for shell elements in June, and the August update extended Low Latency Profile to app launches. In our before-and-after testing on one PC, apps like WhatsApp, Calculator, and even the RAM-hogging Weather app opened much faster.

However, Microsoft used controlled feature rollout, so two PCs on one update could get different results, and many people had to force it with ViVeTool. Even when we tested 26H2 in August, the version bump didn’t switch features on by itself.

Since 26H2 now rolls up everything from the past few months, both the shell and app launch boosts should be on for most PCs that move to it, which makes the whole OS feel snappier.

My 16GB Windows 11 laptop is suddenly using much less RAM after 26H2...On Reddit, a user reported that RAM usage on boot dropped by around 2GB after the 26H2 update, on a PC with 48GB of RAM. Another user on X said usage on their 32GB PC fell from 9.1GB on 25H2 to 7.5GB on 26H2, while an 8GB PC owner saw no change at all, still at 96%.

My RAM numbers look similar. Right after a restart, Task Manager showed 7.0GB in use out of 15.7GB, which is 45%, with 4GB cached and 8.8GB available. Before, this laptop would often cross 10GB before I even opened an app. The 7GB shown could’ve been smaller if it weren’t for the startup apps: Lenovo Smart Connect, Plex, Quick Share, PowerToys, WhatsApp, Teams, and more.

Keep in mind that high RAM usage isn’t always bad. Windows uses free memory to cache data it thinks you’ll need, and releases it when an app asks for more.

To push it, I opened WhatsApp, Teams, and Outlook alongside Edge with a bunch of heavy tabs, and played a 4K 60fps video on YouTube. Of course, this is a stress test scenario, but even then, Memory usage hovered around 87%. I know that my PC would have touched 95% in milder tests.

Out of curiosity, I then added Microsoft Store, Paint, Lenovo Vantage, Notepad, Spotify, and Raindrop.io, and memory usage still stopped at 89%, roughly 14GB. Interestingly, Edge seemed to give up memory as I opened more apps, and took more back once I closed them.

Of course, this would have easily gone above 95% RAM a few weeks ago, but we’re unaware of what part of Windows Microsoft optimized here.

I’ve always regretted buying a laptop with soldered RAM, because it was always stuck between 90% and 95%. Now, maybe I don’t need a new laptop just yet!

Obviously, your mileage may vary. Startup apps, cached memory, app versions, and how Edge manages its tabs can all change these numbers, so this doesn’t prove 26H2 itself cut RAM usage, especially since Microsoft hasn’t uttered a word that they already improved it.

We all assumed the RAM optimizations were coming later.

Microsoft promised to reduce Windows 11’s RAM usage, but 26H2 isn’t the whole story...Back in March, Microsoft said Windows 11 would run faster under heavy load and reduce RAM usage. In July, they made memory optimization for 8GB and above an official priority, and at IFA, they promised faster boot and better 8GB performance.

Pavan Davuluri also said the rising cost of memory is why Microsoft is working on the memory manager, memory compression, WinUI, and WebView2. WinUI just got a memory growth fix, too.

It’s strange that Microsoft didn’t list any RAM reduction among 26H2’s changes, because this is a very welcome change.

26H2 is probably just when months of optimization work became visible on many users’ PCs. But I have always been enabling features with ViVeTool, so that isn’t what happened here, at least for me.

The 8GB reports also suggest the work isn’t fully there yet, and our 26H2 test PC in August only had 4GB of RAM, which may be why we didn’t notice anything then.

Windows 11 has quietly been getting faster all year...Microsoft’s 26H2 page also calls out a faster and more responsive File Explorer, after months of work that made it faster without preloading. This, combined with Low Latency Profile, a faster Search, WinUI fixes, and memory work, means you get many small changes while using the OS.

However, using a PC involves third-party apps, and that’s where we need more optimizations. Electron apps like Discord are notoriously RAM-hungry. WhatsApp is probably the most popular, least optimized app in Windows. With Microsoft deleting its 32GB RAM recommendation as memory gets expensive, we hope developers take note and go the native route with app development. AI has enabled making WinUI apps easier than ever, especially with Microsoft wholeheartedly supporting vibe coding.

26H2 is a tiny enablement package, but Microsoft has spent 2026 fixing Windows 11 piece by piece. While Low Latency Profile is easy to verify, RAM drop isn’t.

I’m hoping Microsoft has more to say about RAM optimization in the upcoming October 7 event. Either way, my PC feels lighter than it has in a long time, and that gives me a lot of hope for Windows 11.

mundophone

TECH

Intel's 28-Core Ultra 9 4970K BFC leaks as flagship Nova Lake CPU

There's been a lot of speculation over the last few months on the matter of what exactly Intel was going to name its Nova Lake processors. Most people seemed satisfied to expect "Core Ultra 400 Series", which would make sense in light of its latest laptop CPUs. Instead, apparently, Intel is going with the Core Ultra 4000 series, at least on desktop. We heard about the Core Ultra 9 4950K before, but a new list from leaker LC Tech Leaks seems to corroborate that with a list of SKUs including six more models.

The list leaked by Laurents Choice was covered up in JPEG artifacts and ordered in a confusing way; you can see the original list by clicking the image above. However, the image directly embedded above is a cleaned-up version that has the same data, simply re-ordered in a more intuitive way. Starting from the top of the list we have the most exciting SKU of the bunch: a supposed Intel Core Ultra 9 4970K BFC.

What is "BFC"? Well, probably "Big F-ing Cache", if we were to guess. Given the gaming-focused nature of AMD's big-cache CPUs, the much-beloved "X3D" processors, it only makes sense for Intel's competing parts with the "Big Last Level Cache" (BLLC) feature to reference the grand-daddy of the FPS genre, DOOM, and its legendary "Big F-ing Gun". That part will apparently come with 8 P-cores, 16 E-cores, and 4 LP-E cores, which matches the 28-core prediction that's been floating around for some time.

Notably, many processors in the list do not mention the 4 LP E-cores present in the BFC SKUs. That would contradict the specifications given earlier by Jaykihn, but the Intel-specific leaker specifically replied to the LC Tech Leaks tweet saying exactly that: "the non-DS (non-bLLC/BFC) processors featured here also have LPE-cores and vPro support ... it just wasn't listed here, nothing deeper to it."

For its part, that Core Ultra 9 4950K looks extremely similar to the Core Ultra 9 285K that tops the current Arrow Lake family, so it will make a good candidate for generational comparisons. Of course, it's the "BFC" chips that we're really interested in benchmarking. We're dying to know if Intel's finest competes not only with the Ryzen 7 9850X3D but also with whatever AMD's cooking up for its next-generation Olympic Ridge processors.

The lineup is divided into models ranging from Core Ultra 5 to Core Ultra 9, confirming that the giant is set to revise its three-digit numbering scheme and return to the old four-digit standard, as previously rumored. According to the post, the lineup looks like this:

Core Ultra 9: Led by the 4970K BFC and 4950K models, both featuring 28 cores (8 performance, 16 efficiency, and 4 low-power) and a 125W TDP. The efficiency-focused option is the 4900 BFC (65W and 22 cores).

Core Ultra 7: Comprising the 4870K BFC and 4850K, delivering 24 cores (8+12+4) and 125W.

Core Ultra 5: Consisting of the 4650K and 4650KF, with 22 cores (6+12+4) and 125W. As usual, the KF model is the only one on the list lacking integrated graphics with 32 Execution Units (EUs).

The detail that really stands out in the data is the "BFC" suffix; although its exact meaning remains unknown, all signs point to this code identifying processors equipped with bLLC (Big Last Level Cache)—a cache technology designed to compete with AMD's X3D series.

Since these processors utilize a single-chiplet design, it is speculated that the BFC-equipped chips mentioned here could reach up to 144 MB of cache—a significant boost for tasks requiring rapid data retrieval, such as demanding games. However, even more powerful options are expected, featuring 52 cores and 288 MB of bLLC. Echoing LC Tech Leaks, another popular leaker, Jaykihn, confirmed that Intel’s new generation will indeed return to four-digit model numbers—though not across the entire lineup. Both desktop models and the HX laptop series—which utilizes desktop-grade hardware—will adopt this change. Meanwhile, other laptop-focused solutions will retain the current three-digit naming scheme.

The new Intel Nova Lake processors are expected to be previewed later this year, although the official unveiling is likely slated for January at CES 2027. The launch will require a new platform featuring the LGA1954 socket and Z990/Z970 motherboards, but significant improvements and longer-lasting support are anticipated this time around. The rollout is expected to occur in stages throughout the coming year.

There are several interesting details when looking at the range broadly. Most notably is the four-number model identifier, which Intel didn't use with Arrow Lake chips like the Core Ultra 7 270K Plus. This larger identifier has been previously rumored, and it makes sense if the lineup above is indeed real. There's quite a bit of specificity in this stack, and something like the "Core Ultra 9 497K" doesn't signal what a "Core Ultra 9 4970K" does.

Regardless of naming, the SKU table shows three models with BFC, two of which fall under the Core Ultra 9 umbrella. There isn't a Core Ultra 5 BFC option. The most interesting model is the Core Ultra 9 4900 BFC, which matches the 22-core count of the Core Ultra 5 models, though with the addition of BFC and a lower 65W TDP. Given this chip doesn't have a K suffix, it looks like a specialized, low-power gaming chip, perhaps for small form factor desktops.

In addition, the Core Ultra 5 model is the only one with an F suffix, noting that it lacks integrated graphics. As we saw with the Arrow Lake refresh, Intel released a Core Ultra 5 250KF Plus, though it never gave the 270K Plus the KF treatment.

According to the table, Intel could use "BFC" to note chips with a larger L3 cache, something that's been heavily rumored for Nova Lake as AMD's X3D chips dominate gaming in our CPU benchmark hierarchy. This is the first time we've heard it referred to as BFC; however, with previous rumors referring to the additional cache as bLLC.

As for what BFC stands for, there are a few possible candidates, and we'll leave it to your imagination as to what words that start with "F" could fit between "Big" and "Cache."

mundophone

 

TECH

Brown University engineers build brain-inspired imaging system that can ‘see’ through fog

Researchers from the Brown University School of Engineering have developed a new imaging system that can see and track moving objects in partially opaque environments like dense fog or muddy water. 

The technique, described in a study published in Advanced Science, combines a dynamic vision sensor — a camera with pixels that report only changes in brightness, rather than capturing full frames — with a spiking neural network computer model that reconstructs the moving objects spied by the sensor. In laboratory tests, the system’s reconstructed images matched the hidden targets with structural similarity scores of up to 96% in turbid water and drifting fog that would hide the objects from a traditional camera, while they simultaneously tracked the targets’ positions as they moved. 

The brain-inspired system is the first end-to-end technique that can both track and image randomly moving objects hidden by such dense scattering media, the researchers say. 

“The reason it’s hard to see objects in fog is that the light bouncing off of objects gets scattered by the fog before it reaches our eyes or the camera lens,” said Ning Zhang, a postdoctoral researcher in engineering at Brown. “This research project is about using novel imaging cameras and a brain-inspired model that can filter out that scattering process and reconstruct what's behind the fog.”

The system could eventually have a variety of real-world applications, including for self-driving cars and other autonomous vehicles, drone-based search and rescue efforts during storms or wildfires, or even medical imaging, the researchers say. 

Inspired by the brain...To develop the technique, the researchers took cues from the human visual system, which can process images with remarkable speed and efficiency. Rather than collecting a full picture all at once like a video camera, the retina responds mainly to changes in the visual field — edges, motion and flicker —and feeds that information to the brain, which builds a reconstruction of the scene over time. When it senses a change, it sends neural spikes to the brain, which are used to update the brain’s reconstruction of the world. 

The researchers were able to harness a similar architecture to create a system that processes images quickly and efficiently, but that outperforms human vision in foggy or turbid conditions. The system starts with a dynamic vision sensor that can detect light-intensity changes in individual pixels within the visual field. Because each pixel fires only when the brightness it sees changes by more than a set threshold, the sensor registers a moving object even when much of the light has been scattered by fog or some other scattering medium. And because the scattering background changes far more slowly than the moving target, the sensor largely ignores the fog and responds mainly to light that has interacted with the object. What it records is still a shapeless cloud of spikes, which is where the spiking neural network comes in. 

“The human eye is quite good at motion detection, but there are ways in which a camera, specifically a dynamic vision sensor, can be better, including that it’s sensitive to near-infrared light, which the human eye cannot see,” said Arto Nurmikko, a professor of engineering at Brown and the study’s senior author. “It captures information only about a moving object, and in doing so effectively filters out some of the fogginess while generating an output where relevant information is coded as trains of asynchronous spikes, much as the biological retina acquires data in a form the brain’s visual cortex understands.”

The key innovation in the work is a neuromorphic computer model mimicking the visual cortex: a deep spiking neural network that takes data from the vision sensor and reconstructs images of randomly moving, normally unrecognizable objects while tracking their trajectories in fractions of a second. The network runs on spikes as the fundamental unit of information. 

To test the system, the researchers projected images of moving alpha-numeric characters and silhouettes of different species of flying birds through an obscuring fog chamber or a tank of turbid water. The experiments showed that the system was able to image and track the randomly moving objects unrecognized by a standard camera system or the human eye. 

The system is also highly energy efficient. The sensor itself draws only tens of milliwatts. The spiking network uses a fraction of the energy used by a conventional neural network because it computes only on sparse spikes rather than dense frames.

The approach has limits, the researchers note. Because the sensor responds only to change, an object has to be moving relative to the camera to be seen, and the current system recovers an object’s silhouette rather than a full grayscale image. The sensor also loses sensitivity in very dim light. The team is now exploring a light-intensifier front end for low-light conditions, as well as depth-resolved approaches that can extend the method to three-dimensional targets — for example, by timing light’s travel time, or by pairing two event cameras like a pair of eyes.

“This could be useful anywhere where light-scattering by the surrounding medium is a serious problem — self-driving cars, search-and-rescue drones and underwater navigation are a few examples,” Zhang said. “The goal is to push the boundary of what we can perceive, to improve safety, healthcare and beyond.”

Engineers at Brown University have built a new brain-inspired imaging system that can track and "see" moving objects through dense fog and murky water with up to 96% accuracy.

The research was published in Advanced Science in October 2026. It solves a major problem for traditional cameras: light bouncing off fog instead of reaching the lens.

How the system works:

• Dynamic vision sensor: A special camera that records only changes in brightness (called events) instead of capturing normal video frames.

• Spiking neural network: A computer model inspired by the human brain. It processes sparse electrical signals (spikes) rather than dense data blocks.

• Filtering: The system ignores the random light scattering caused by water droplets in the fog and reconstructs what hides behind it.

Key benefits:

• High accuracy: Reconstructed images match hidden targets with up to 96% structural similarity in lab tests.

• Simultaneous tracking: It is the first system that can both image and track randomly moving objects hidden in dense fog at the same time.

• Energy efficient: The sensor uses only tens of milliwatts, and the brain-like network uses a fraction of the energy of traditional AI models.

Current limitations:

• Movement required: Because the camera reacts only to change, stationary objects cannot be seen.

• Silhouettes only: The system currently recovers object outlines (silhouettes) instead of full grayscale pictures.

• Low light: The sensor loses sensitivity in very dim environments.

Potential uses:

• Self-driving cars: Helping autonomous vehicles navigate safely through heavy mist or rain.

• Search and rescue: Guiding drones during severe storms or thick wildfires.

• Underwater navigation: Helping robots see through murky water.

Brown University

TECH

GeForce RTX 5070 Ti memory gets overclocked to a scorching 39 Gbps

The GDDR7 memory used on the GeForce RTX 50 series GPUs may have considerably more memory overclocking headroom than NVIDIAs official specifications suggest, with one enthusiast pushing Samsung GDDR7 memory all the way from 28 Gbps to a scorching 39 Gbps, coincidentally a 39% overclock. This came about thanks to the mVolt+ Extreme overclock tool, which can apparently bypass the memory overclocking limits imposed by conventional GPU tuning software. A Gigabyte GeForce RTX 5070 Ti Windforce owner reports successfully running the card's Samsung GDDR7 at a +5500 MHz memory offset, corresponding to a 39 Gbps data rate.

The mVolt+ Extreme tool pushes memory overclocking limits... The mVolt+ Extreme utility stands out from conventional GPU overclocking tools by allowing the hardware to operate beyond manufacturer-imposed restrictions.

The tool had already demonstrated the ability to unlock a TDP of up to 700W for the GeForce RTX 5090 without shunt modding (though using it this way is at your own risk—given that the card tends to melt things under normal conditions, imagine the consequences with a higher TDP), surpassing the stock limit of approximately 600W.

On the RTX 5070 Ti, mVolt+ Extreme enabled a memory offset of +5500 MHz, whereas most conventional utilities allow for a maximum additional offset of +3000 MHz.

Another user in the same Reddit thread confirmed stable performance at +5500 MHz on the RTX 5070 Ti but noted that 6000 MHz remained unstable.

Results vary across users and games... Performance improvements in games were considered modest, with the exception of a 20% gain observed in Steel Nomad.

The +5500 MHz offset boosts memory speed from 28 Gbps to 39 Gbps—a 39% increase—resulting in a bandwidth of 1248 GB/s compared to the stock 896 GB/s. However, in practice, this does not translate to a proportional gain in frames per second.

Other users who replicated the procedure achieved different results: some exceeded 5000 MHz, while others remained in the 4000 to 4500 MHz range.

If you're wondering how adding 5500 MHz is even possible, and also wondering how it added 11 Gbps to the transfer rate, let me explain. GDDR7 transfers sixteen bits per clock thanks to its design. That "+5500 MHz" number is actually even more confusing, because it's an intermediate data rate number. The actual clock rate at 39 Gbps is 2437.5 MHz, and the pre-DDR data rate is 19500 MT/s. That's an increase of 5500 MT/s over the stock 14,000 MT/s, which turns into 28 Gbps when you account for DDR signaling. Here, this chart might help:

As you can see, the bandwidth increase is easier to understand. The GeForce RTX 5070 Ti has a 256-bit memory bus, so its stock 28-Gbps memory provides 896 GB/s of theoretical bandwidth. At 39 Gbps, that rises to 1,248 GB/s—a 39.3% increase, essentially matching the increase in memory data rate. That pushes the memory bandwidth well ahead of the GeForce RTX 5080, and in fact within striking distance of the GeForce RTX 5090 D V2, the slightly-neutered RTX 5090 that was built for the Chinese market before Beijing banned the boards from entering the country.

Actually getting a GeForce RTX 5070 Ti to run its RAM at 39 Gbps is quite a feat, as you'd expect. The Redditor says +5500 worked in Cyberpunk 2077 with path tracing enabled for 30 minutes, while +6000 immediately caused the graphics driver to restart. Other users have reported lower ceilings, with examples around +4500 and +5000, and at least one user reporting artifacts beyond +5500. That makes it clear that results will vary from card to card and, potentially, depending on the memory chips installed on the board.

The performance gains are also nowhere near as dramatic as the memory bandwidth increase. The original tester, /u/Sad-Victory-8319, reported roughly 1 FPS of additional performance in Cyberpunk 2077 for every +1000 MHz of memory offset, while another user reported a roughly 1.5% to 2% increase in 3DMark Speed Way when moving from +3000 to +5000. In the former test, +5500 represented roughly a 5- to 6-FPS improvement over stock memory settings. A bit unimpressive, but not unexpected; GDDR7 is so fast even at stock settings that these GPUs are almost never bandwidth-bound.

Interestingly, despite that they use the same Samsung GDDR7 memory, similar experimentation on GeForce RTX 5090 cards appears to hit a lower ceiling. Users in the Overclock.net "RTX 5090 Owners Club" thread have also been toying with the mVolt+ tool, and they report getting to roughly +4000 MHz before driver resets become an issue. Why the 5070 Ti appears capable of going substantially further isn't yet clear, but it could have to do with the half-width memory bus or the considerably higher strain on the RTX 5090's power delivery hardware.

Either way, the discovery here is notable because it suggests at least some, and possibly a majority of RTX 50-series cards have a considerable amount of untapped GDDR7 frequency headroom. Whether running a gaming GPU at these kinds of memory speeds is sensible for long-term use is a separate question, particularly considering the fairly minimal performance gains to be found from even extreme overclocks like this. Still, it's hard not to be a little impressed by the sight of "39 Gbps" memory on a GeForce RTX 5070 Ti.

mundophone

 

TECH

Visual illusion reveals what today’s AI vision is missing – York University study

Our eyes do not always tell us exactly where things are – and that may be a feature of how biological vision works, rather than simply a flaw. A new study by York University researchers uses a common illusion to ask if artificial intelligence is meant to see more like us, should it make some of the same systematic perceptual “mistakes”?

For example, after staring at something moving steadily in one direction, a stationary object viewed immediately afterward can appear slightly displaced in the opposite direction. This well-known visual illusion, called a motion aftereffect, gives scientists an unusual window into the computations underlying perception: the image itself has not moved, but our experience of where it is has changed.

“Today’s AI vision systems are impressive, but they still do not always see the world the way we do. This study captures the promise of NeuroAI and what it can do when neuroscience and artificial intelligence are brought together. By using smart experiments to reveal the computations biological vision uses and AI still lacks, we can use those insights to build better, more brain-like artificial systems,” says senior author York Assistant Professor Kohitij Kar, the Canada Research Chair in Visual Neuroscience and a member of York’s Centre for Vision Research and Centre for Integrative and Applied Neuroscience.

Current AI vision systems can often determine where an object is accurately, but they generally do not reproduce the way recent visual experience can reshape that answer. In humans, staring at motion can make a subsequently viewed stationary object appear displaced even though its pixels have not moved. The researchers found a corresponding change in the primate visual cortex – but not in the AI models they tested.

To gain better insight into the issue, the researchers examined whether artificial neural network models capture the same history-dependent changes in spatial representations seen in biological vision, or whether their position representations primarily reflect the physical properties of the image. The researchers, including the paper’s first author and York graduate student Elizaveta Yakubovskaya, used precise measurements to find out where AI differs from biological vision and how that gap could be bridged.

“Combining recordings from primate visual cortex with human perception experiments, we used motion adaptation to induce a visual illusion and make a stationary object appear slightly shifted in position, then asked whether the brain and AI showed the same effect. Human observers reported the illusion, and neural representations of position in the primate inferior temporal (IT) cortex shifted in the same direction, even though the image itself had not changed,” says Yakubovskaya.

The researchers leveraged motion adaptation to show where perceived and pixel-based positions diverge, allowing them to test the behavioral relevance of IT codes. The findings not only further our understanding of IT’s role in spatial information encoding but provide a new benchmark to evaluate dynamic vision models.

“There is a growing question in AI about whether increasingly capable systems will become more like us or increasingly different from us,” says Kar of the Faculty of Science and a member of the York-led Connected Minds. “If we want AI that works with humans and understands the world in more human-compatible ways, we cannot focus only on whether it gets the right answer. We also need to understand the computations that produce human perception and behavior. Neuroscience gives us a way to discover those computations and, potentially, build them into AI,” adds Kar.

Visual illusions reveal that today’s AI vision is missing history-dependent, dynamic perceptual computations that allow biological brains to interpret a changing world rather than just recording static pixels.

A recent study from York University published in Current Biology highlights a fundamental gap between artificial intelligence and primate vision:

The core findings:

• The Illusion Divide: When humans and macaque monkeys look at a stationary object after experiencing motion adaptation (a motion aftereffect), they perceive the object as physically shifted in position, even though the image hasn’t changed.

• Neural Mirroring: Recordings from the primate inferior temporal (IT) cortex show that biological brain cells actually shift their spatial position codes to match this subjective, illusory perception.

• AI Failure: Current state-of-the-art artificial neural networks and AI vision models do not replicate this shift. They remain rigid and strictly tethered to static pixel coordinates.

What AI is missing:

• Passive vs. Active Processing: Human eyes and primate brains do not operate like digital cameras that capture raw, objective pixels frame by frame.

• Temporal Integration: Biological vision actively blends immediate visual history and recent temporal experience into its spatial calculations.

• Perceptual Alignment: Illusions are not just computational mistakes; they are the functional byproduct of an adaptive brain that predicts and prioritizes what is useful for survival based on context. AI lacks this fluid, context-aware framework

A new study from York University reveals that today’s AI vision lacks history-dependent, dynamic perceptual computations that allow biological brains to shift object position based on recent visual experience.

The illusion and the experiment:

• The motion aftereffect: When humans or primates watch steady motion in one direction and then look at a stationary object, the object appears slightly shifted in the opposite direction.

• The biological response: Neural recordings from the macaque inferior temporal (IT) cortex show that the brain's spatial position codes shift dynamically to mirror this illusion, even though the physical image has not changed.

• The AI blind spot: When tested on the same visual paradigm, standard artificial vision networks show no adaptation; their internal coordinates remain rigid and strictly tethered to static pixels.

What AI vision is missing:

• Adaptive processing: Biological vision does not act like a passive digital camera cataloging raw pixels. It actively integrates immediate visual history into how it constructs reality.

• Perceptually aligned coordinates: AI lacks the capacity to encode position based on dynamic, context-aware neural computations.

• A new neuroAI benchmark: Researchers suggest that future AI must be trained on these adaptable perceptual mechanisms—rather than just static pixel accuracy—to work safely and intuitively alongside humans

York University

SAMSUNG

Samsung's Exynos 2700 enters mass production with 10% higher output than Exynos 2600, says leaker

Tipster Ice Universe claims Samsung has begun front-end wafer mass production of the Exynos 2700, a 2nm chipset built on the company's SF2P node. Initial output is reportedly 10% higher than the Exynos 2600's, and internal Geekbench 6 runs allegedly show a 9.5% multi-core lead over Qualcomm's Snapdragon 8 Elite Extreme Gen 6.

Samsung has reportedly started mass production of the Exynos 2700, its next flagship smartphone chipset, according to tipster Ice Universe. The leaker claims the 2nm processor has entered front-end wafer production, moving beyond the limited sampling stage that comes before a commercial ramp.

Ice Universe further alleges that this initial run is roughly 10% larger than the Exynos 2600's at the same point in its cycle. The Exynos 2700 is expected to be fabricated on SF2P, an enhanced version of the SF2 process behind the Exynos 2600. Samsung announced the Exynos 2600 in December 2025 as the world's first 2nm chipset built with gate-all-around (GAA) transistors. It uses a 10-core CPU in a one-plus-three-plus-six layout, led by an Arm C1-Ultra prime core clocked at 3.80GHz.

A die shot of the Exynos 2700, leaked by semiconductor research firm SemiAnalysis in early September, points to a reworked 10-core CPU with two prime cores instead of one. The chip reportedly pairs an Arm C2-Ultra core at 4.24GHz and four C2-Pro cores at 3.74GHz with a C1-Ultra core at 3.36GHz and four C1-Pro cores at 2.88GHz. The die also indicates LPDDR6 memory support, 24MB of system-level cache, a large NPU and an Xclipse 970 GPU with the same eight WGPs as the Exynos 2600's Xclipse 960.

Samsung's internal testing reportedly places the Exynos 2700 9.5% ahead of Qualcomm's Snapdragon 8 Elite Extreme Gen 6 in the Geekbench 6 multi-core benchmark, partly due to a reported 4.00GHz target frequency for the chip's prime core. The leaked results include no power consumption figures or other efficiency metrics, so the performance claim cannot yet be weighed against energy use.

Qualcomm chipsets are still expected to power Galaxy S27 models sold in the US and China. Samsung's MX mobile division is reportedly assessing performance, thermals, cost, and SF2P manufacturing capacity before deciding whether a larger Exynos 2700 order makes economic sense. Staying with the Snapdragon 8 Elite Extreme Gen 6 and Snapdragon 8 Elite Gen 6 also carries a high cost, since both are manufactured on TSMC's 2nm N2P process. Samsung has not confirmed whether the Galaxy S27 Ultra will use the Exynos 2700 in any market.

A veritable "internal war" is currently underway between Samsung System LSI and the MX division, as the former advocates for the inclusion of the Exynos 2700 in the Galaxy S27 Ultra.

Samsung MX, on the other hand, is taking a much more cautious approach, evaluating factors such as performance, thermal behavior, cost, and production capacity regarding the 2-nanometer process.

Nevertheless, there is considerable excitement within the South Korean giant, particularly after the Exynos 2700 achieved a multi-core score 9.5% higher than that of the Snapdragon 8 Elite Extreme Gen 6 on Geekbench.

Regarding the Geekbench results, the Qualcomm chip scored 12,856 points, whereas the Exynos 2700 managed to reach 14,000 points.

Samsung is not currently commenting on the leaks, but component production for the Galaxy S27 has already begun, and development of One UI 9.5 is progressing rapidly.

The Samsung Galaxy S27 Pro – along with the vanilla and plus models – will use the Exynos 2700 in Korea and Europe. These models will switch to a Snapdragon 8 Elite Gen 6 chip for the US and China.

As for the Galaxy S27 Ultra, that one is still up in the air. Samsung System LSI, which designs the Exynos chips, has confidence in the 2700 and is pushing to get it adopted on the top-tier S-phone. This would help the division’s earnings, of course, but it will also signal that the chip is good enough for an Ultra-class flagship.

Samsung MX, the division building the phones, has the final say, however. Right now it is considering multiple factors – benchmark and thermal performance as well as the price of Qualcomm’s chips and the production capacity for Exynos 2700 chips.

A while back, the news came out that internal testing had shown that the Exynos 2700 beats the Snapdragon 8 Elite Extreme Gen 6 on the multi-core Geekbench test by 9.5%. However, the Snapdragon still has the edge in the single-core test.

The Exynos 2700 will be fabbed on Samsung’s SF2P, a second generation 2nm Gate-All-Around (GAA) node. Compared to the first-gen node (SF2), which was used for the Exynos 2600, SF2P offers a 12% performance improvement, a 25% reduction in power usage and an 8% decrease in chip area. There will be some architectural improvements too, of course, with the Exynos 2700 likely using Arm C2 CPU cores (the 2600 used C1 cores).

Apparently, it takes three-four months from the moment a chipset goes into mass production to having the final product ready. The timing works out just right for a February or March launch of the Galaxy S27 series.

Samsung typically unveils new Exynos flagships shortly before the turn of the year, as it did with the Exynos 2600 on 19 December 2025. An official Exynos 2700 announcement is therefore expected in December 2026, or possibly earlier.

Galaxy S27...The Galaxy S27 Pro will be the new addition to the family. The model will feature a smaller screen than the S27+, yet it is expected to be a more advanced option, with features closer to those of the Ultra. The aim is to offer a premium experience in a more compact device.

Samsung Display is also expected to begin mass production of OLED screens for the new generation this month. The company has reportedly set an annual target of 47 to 48 million panels for the Galaxy S27 line—a figure more than 10% higher than the estimated volume for the Galaxy S26 series.

Privacy Display may come to more models... Another expected highlight is the expansion of Privacy Display, a feature that makes it difficult for people standing next to the user to view the screen.

In the current generation, the technology is reportedly restricted to the Galaxy S26 Ultra. For the Galaxy S27 line, it is expected to appear on the S27 Pro as well. The S27 and S27+ models are not expected to receive the feature. Notably, the Galaxy S27 Ultra might only mask part of the screen.

Demand for the technology in the current generation reportedly influenced Samsung's plans to expand its presence across the smartphone family.

Costs...Despite the expected increase in component production, Samsung intends to reuse some parts from the Galaxy S26 line for the new devices.

The company also reportedly has ambitious plans for the Galaxy S27 Pro. Samsung's display division expects to produce between 2 million and 3 million panels for the model by the end of this year, and between 4 million and 5 million next year. It is worth noting that while the Galaxy S27 may be significantly more powerful, the price is also expected to rise.

mundophone

 

TECH

Cement-based supercapacitors could power next-generation ‘smart’ buildings

Imagine a future in which the very concrete beneath your feet and around your windows does more than hold a building upright. In that future, the same material that forms slabs, stairs, and load-bearing walls also stores the electricity generated by rooftop solar panels, releasing it in bursts to light corridors, run sensors, and keep emergency systems alive during outages. That vision moved a significant step closer to reality with the publication of a peer-reviewed study in ACS Nano, in which researchers report an efficient, energy-storing cement supercapacitor that, crucially, performs as well as commercial concrete in mechanical tests. 

The work, led by corresponding author Jing Zhong together with colleagues Wencai Ren and Haiping Wu, demonstrates that cement need not be a passive, inert filler in the built environment. Instead, it can be engineered into an active electrochemical component, one that is 3D printable, structurally robust, and capable of powering real electronic devices.

To understand why this result matters, it helps to distinguish supercapacitors from the lithium-ion batteries that dominate consumer electronics. Batteries store large amounts of energy through slow chemical reactions, which is why they take hours to charge and degrade over hundreds or thousands of cycles. Supercapacitors, by contrast, store energy electrostatically, in the electric field that forms at the interface between an electrode and an electrolyte.

They hold relatively small amounts of energy compared with batteries, but they take that energy in and release it extremely rapidly, and in some designs they can survive millions of charge-discharge cycles without significant degradation. That combination of speed and durability makes them attractive for applications where power is needed in short, repeated bursts rather than as a long-duration reserve. For a building fitted with solar panels, supercapacitors embedded in the structure could absorb surges of renewable electricity as they arrive and discharge them on demand, smoothing the mismatch between when energy is generated and when it is actually used.

The central challenge in turning cement into a supercapacitor is that ordinary concrete is a poor electrical conductor. Cement hydrates into a porous, mineral-rich matrix that is excellent at bearing compressive loads but terrible at shuttling electrons and ions. The research team’s solution was to build conductivity directly into the material. They mixed carbon nanotubes, carbon black, and cement together to form a printable electrode ink. Carbon nanotubes are cylindrical structures of carbon atoms just nanometers in diameter, renowned for their exceptional electrical conductivity and mechanical strength. Carbon black, a much cheaper amorphous form of carbon, provides additional conductive pathways and helps form a percolating network throughout the composite. Blended into wet cement, these carbon additives transform the paste from an insulator into an electrode material, while the cement itself continues to cure and harden much as it would in any construction application.

Geometry proved to be just as important as chemistry. Using a 3D printer, the team deposited the electrode ink onto a small concrete slab in a pattern resembling interlocked fingers, a configuration known in electrochemistry as an interdigitated electrode design. In this arrangement, two sets of parallel electrode fingers alternate with one another, like the interlaced fingers of two hands held together without touching. The significance of this geometry lies in the distance that charged ions must travel. In a conventional two-plate capacitor, ions in the electrolyte have to migrate across the entire gap separating the electrodes, which slows the device down and wastes energy as internal resistance. With interdigitated electrodes, every finger of one polarity sits immediately adjacent to fingers of the opposite polarity, so ions move only short distances laterally. As the researchers note, this shortened travel distance made the overall supercapacitor more efficient than previous iterations of cement-based energy storage, which had struggled with exactly this ionic transport bottleneck.

A remarkable feature of the design is that the electrolyte is not added as a separate liquid component but arises from the cement itself. As the cement within the slab hydrated, the chemical reaction that gives concrete its strength, its pores filled with water and dissolved ions that could easily travel between the electrodes. In other words, the very process that turns printable ink into hardened concrete simultaneously creates the ionic medium the supercapacitor needs to function. This monolithic approach, in which electrode, electrolyte, and structural support are one continuous piece of material, is what the study’s title refers to in describing monolithic 3D-printed interdigitated cement-based supercapacitors for structural energy storage. It eliminates the interfaces and packaging that complicate conventional devices and means the energy-storage function can, in principle, be printed wherever a builder wants it within a larger concrete element.

Of course, an energy-storage device embedded in a building is useless if it compromises the building’s safety. Concrete in structural applications must meet strict compressive strength standards, and any multifunctional material has to prove it can carry loads without failing. The researchers therefore subjected their cement supercapacitor to mechanical testing and found that its compressive strength was comparable to that of commercial concrete used in slabs and stairs. This is the result that elevates the work from a laboratory curiosity toward practical relevance. Previous attempts at cement-based energy storage often faced a trade-off, in which adding conductive carbon or porosity weakened the material. Here, the team achieved a device that is simultaneously an electrode, an electrolyte-filled capacitor, and a structural material meeting the expectations placed on ordinary construction concrete.

The researchers also demonstrated that their devices do real electrical work. Three supercapacitors printed on the same slab and wired together successfully powered a small array of LEDs, a modest demonstration on its own but an important proof of concept showing that multiple devices fabricated from a single concrete element can be connected into a functioning energy system. Looking ahead, the team envisions these supercapacitors powering everything from emergency lighting to self-powered sensors. That second application deserves particular attention. Modern buildings increasingly depend on distributed sensor networks that monitor temperature, occupancy, air quality, and structural health, and wiring or periodically replacing batteries for thousands of such sensors is costly and cumbersome. Sensors embedded in or on concrete that draw their operating power from the concrete itself would remove that maintenance burden entirely, enabling dense, self-sustaining monitoring of the built environment.

Like any emerging technology, the cement supercapacitor has identified limits, and the team was candid about one of them. The devices operated stably under moderate heating and cooling, but at around zero degrees Fahrenheit, or minus 18 degrees Celsius, their performance started to wane. This matters because the electrolyte in the system is water-based, arising from the hydration of the cement, and water loses its ionic mobility as it approaches freezing. In cold climates, where buildings must perform year-round, a supercapacitor that fades in deep winter would be a serious limitation. The researchers state that future research will focus on fortifying the supercapacitors against cold-weather conditions, an engineering challenge that will likely involve strategies to keep the pore solution ionically active at low temperatures. It is a reminder that multifunctional materials must satisfy the demands of every function they take on, including environmental resilience across the full range of conditions a building experiences.

The broader implications reach toward what researchers call smart buildings, structures that generate, store, and manage energy within their own fabric. As Jing Zhong explains, if building materials could not only support structures but also store energy, sense their surroundings, and even interact with people, buildings would become more than passive shelters. They could become truly smart environments. The study, published in ACS Nano under the title Monolithic 3D-Printed Interdigitated Cement-Based Supercapacitors for Structural Energy Storage, was supported by funding from the Guangdong Hailong Construction Technology Company Limited, a subsidiary of China State Construction International Holdings Limited, an indication that the construction industry itself sees commercial potential in structural energy storage. 

Considerable work remains before supercapacitor walls appear on job sites, including scaling from small slabs to full structural elements, integrating renewable generation and power electronics, and solving the cold-weather performance gap. But the foundational demonstration is now on the table: a cement-based supercapacitor that stores energy efficiently, prints into interdigitated geometries, powers LEDs, and matches commercial concrete in compressive strength. The walls of future buildings may not merely shelter their occupants. They may quietly hold the charge that keeps those buildings alive.

Cement-based supercapacitors are emerging as a promising technology to transform passive buildings into active energy storage infrastructure, paving the way for the next generation of smart buildings. Research led by institutions such as MIT (Massachusetts Institute of Technology) combines cement, water, and conductive additives—such as carbon black or graphitic carbon by-products—to create an internal fractal network capable of conducting and storing electricity.

How the technology works: Unlike conventional batteries that rely on slow chemical reactions, supercapacitors store energy electrostatically:

1. Conductive mixture: Carbon black (a highly conductive and affordable material) is mixed with cement and water. Due to the carbon's water-repellent nature, interconnected filaments form as the cement cures.

2. High surface area: This reaction creates a vast internal network of microscopic channels within the concrete block, resulting in an enormous surface area.

3. Electrolyte: The material is soaked in a standard electrolyte (such as potassium chloride), allowing ions to accumulate on the conductive cement plates to retain the charge.

Advantages for smart buildings:

• Integrated Structural Storage: Eliminates the need for rooms dedicated to heavy lithium-ion battery banks. Walls, foundations, and slabs themselves function as the building's energy storage system.

• Renewable Energy Support: Helps smooth out the intermittency of local clean energy sources, such as solar panels and wind turbines, by storing surplus energy generated during the day for use during peak evening hours. 

• Dedicated Power Supply: In recent laboratory tests released in October 2026 by the American Chemical Society (ACS), 3D-printed devices embedded in a concrete slab demonstrated mechanical strength comparable to commercial cement and were capable of powering LED arrays and autonomous sensors.

• Long Lifecycle: Supercapacitors charge and discharge almost instantaneously and withstand tens of thousands of cycles without suffering the severe degradation that affects standard batteries.

Current challenges...Although laboratory advances show that a standard-sized residential foundation could store enough energy to meet a home's daily needs, the technology still faces hurdles before reaching the commercial market. Energy density remains lower than that of lithium batteries, and tests indicate that performance drops in extremely cold climates (near -18°C), necessitating further research into chemically fortifying the material for low-temperature conditions.

ACS Nano