Five years ago, mathematicians Dawei Chen and Quentin Gendron were trying to untangle a difficult area of algebraic geometry involving differentials, elements of calculus used to measure distance along curved surfaces. While working on one theorem, they ran into an unexpected roadblock: Their argument depended on a strange formula from number theory, but they were unable to solve or justify it. In the end, Chen and Gendron wrote a paper presenting their idea as a conjecture, rather than a theorem.

Chen recently spent hours prompting ChatGPT in the hopes of getting the AI to come up with a solution to the still unsolved problem, but it wasn’t working. Then, during a reception at a math conference in Washington, DC, last month, Chen ran into Ken Ono, a well-known mathematician who had recently left his job at the University of Virginia to join Axiom, an artificial intelligence startup cofounded by one of his mentees, Carina Hong.

Chen told Ono about the problem, and the following morning, Ono presented him with a proof, courtesy of his startup’s math-solving AI, AxiomProver. “Everything fell into place naturally after that,” says Chen, who worked with Axiom to write up the proof, which has now been posted to arXiv, a public repository for academic papers.

Axiom’s AI tool found a connection between the problem and a numerical phenomenon first studied in the 19th century. It then devised a proof, which it helpfully verified itself. “What AxiomProver found was something that all the humans had missed,” Ono tells WIRED.

The proof is one of several solutions to unsolved math problems that Axiom says its system has come up with in recent weeks. The AI has not yet solved any of the most famous (or lucrative) problems in the field of mathematics, but it has found answers to questions that have stumped experts in different areas for years. The proofs are evidence of AI’s steadily advancing math abilities. In recent months, other mathematicians have reported using AI tools to explore new ideas and solve existing problems.

The techniques being developed by Axiom may prove useful outside the world of advanced math. For example, the same approaches could be used to develop software that is more resilient to certain kinds of cybersecurity attacks. This would involve using AI to verify that code is provably reliable and trustworthy.

“Math is really the great test ground and sandbox for reality,” says Hong, Axiom’s CEO. “We do believe that there are a lot of pretty important use cases of high commercial value.”

Axiom’s approach involves combining large language models with a proprietary AI system called AxiomProver that is trained to reason through math problems to reach solutions that are provably correct. In 2024, Google demonstrated a similar idea with a system called AlphaProof. Hong says that AxiomSolver incorporates several significant advances and newer techniques.

Ono says the AI-generated proof for the Chen-Gendron conjecture shows how AI can now meaningfully assist professional mathematicians. “This is a new paradigm for proving theorems,” he says.

Axiom’s system is more than just a regular AI model, in that it is able to verify proofs using a specialized mathematical language called Lean. Rather than just search through the literature, this allows AxiomProver to develop genuinely novel ways of solving problems.

Another one of the new proofs generated by AxiomProver demonstrates how the AI is capable of solving math problems entirely on its own. That proof, which has also been described in a paper posted to arXiv, provides a solution to Fel’s Conjecture, which concerns syzygies, or mathematical expressions where numbers line up in algebra. Remarkably, the conjecture involves formulas first found in the notebook of legendary Indian mathematician Srinivasa Ramanujan more than 100 years ago. In this case AxiomProver did not just fill in a missing piece of the puzzle, it devised the proof from start to finish.

Micro RGB TVs should still offer better contrast than their mini-LED rivals, as their smaller size can theoretically offer more dimming zones for better overall black levels. They may also more readily provide naturalistic and granular color shifts, though we won’t know just how much better or different they are until we’ve spent more time with each variety. Samsung’s 2025 prototype was impressive in the short time I spent with it, with fantastic colors, clarity, and brightness. You can currently buy Samsung’s first Micro RGB TV in a 115-inch size for a cool $30,000, but 2026 will see more accessible sizes and (presumably) pricing.

What Is RGBY LED?

Just as we’re all getting our heads around this new era of RGB LED backlighting, Hisense moved the needle once again. After pushing RGB LED backlighting into the spotlight at CES 2025, the company used CES 2026 to reveal its new 116UXS RGB mini-LED TV that adds a new color substructure to RGB’s red, green, and blue modules with the introduction of cyan. (This is not to be confused with Hisense’s new emissive Micro LED TV, which also uses cyan in its color architecture.)

“Cyan sits in the part of the spectrum where human vision is most sensitive to subtle changes and its addition allows the 116UXS to render gradients, tones and transitions with a level of nuance that feels more natural and lifelike,” said Hisense in its press release. The TV is claimed to go above and beyond current RGB LED tech with a stunning 110 percent coverage of the BT.2020 color spec, as well as audio extras like a Devialet Opéra de Paris 6.2.2-channel audio system. We don’t know much more about it or its backlight tech right now, but expect it to come with a very high price tag if and when it becomes available.

What About SQD LED?

TCL was one of the few major panel makers that did not showcase an RGB LED TV at CES 2026. Instead, the company touted another new display technology that advances today’s many quantum-dot-enabled (or QLED) displays, called Super Quantum Dot mini-LED. Debuting with TCL’s new 85-inch X11L SQD mini-LED TV, the new tech blends traditional blue mini-LED backlighting with “newly formulated” Super Quantum Dots, and a new UltraColor Filter.

The TV offers similarly stunning specs to top RGB LED TVs, including up to a claimed 10,000 nits peak brightness and 100 percent of the BT.2020 color spectrum—though the brand also notes the latter is based on “typical performance of tested units” and that “actual results may vary.” TCL goes on to say its new display tech minimizes color artifacts when compared to RGB LED TVs, and that the X11L’s WHVA 2.0 panel is designed to provide a “wide color viewing angle” and enhanced contrast for deep black levels to better compete against OLED displays.

While I haven’t seen the X11L in person yet, it made a big splash at the show, promising to be an exciting new competitor to RGB LED in this rapidly evolving market. The X11L SQD TV is available now in 75-, 85-, and 98-inch sizes starting at $7,000.

RGB TVs You Can Buy in 2026

One of the most exciting things about RGB LED is that it’s already here. In fact, Hisense began selling its first model, the UX Series RGB mini-LED TV, in 2025, albeit in gigantic sizes with similarly gigantic price tags. That’s changing this year, with multiple RGB LED TV models set to be available in more modest sizes, hopefully with more attainable price tags. That’s why we declared 2026 the year of the RGB LED TV. Here are the RGB LED TV models we know about so far:

RGB LED TVs Available Now

Samsung’s original Micro RGB backlit TV claims to offer a backlight system that is “the smallest available in any RGB LED TV” and 100 percent of the BT.2020 color spectrum, Samsung’s glare-free coating, and is powered by Samsung’s Micro RGB AI engine. The short time I spent with this TV’s prototype offered impressive spectacle, with fantastically bright colors. At CES 2026, Samsung also debuted a 130-inch RGB LED TV in a unique form factor the brand calls “the peak of our picture quality innovation,” but it’s unclear when or if this TV will be commercially available.

Hisense’s mini-LED RGB TV was similarly spectacular at CES 2025. It reaches 95 percent of the BT.2020 color gamut from over 20,000 “color control units, and claims up to 8,000 nits of blasting brightness.

RGB LED TVs Coming Soon

Samsung’s latest Micro RGB TV lineup will include 55-, 65-, 75-, 85-, 100-, and 115-inch model sizes, which should mean much more attainable pricing. Along with more accessible screen sizes, highlights include Samsung’s next-gen Micro RGB AI Engine Pro chipset and an upgraded Micro RGB light source with “enhanced” RGB color dimming for improved precision. All the TVs will incorporate Samsung’s glare-free matte screen tech.

Called the “most advanced LCD TV,” LG’s Micro RGB evo will incorporate an upgraded processor and is claimed to achieve 100 percent coverage of BT.2020, the more widely used DCI-P3, and Adobe RGB color gamuts.

With an aim at making RGB LED backlighting tech available to “more homes, more screen sizes, and more price points,” the UR9 and UR8 models will likely be the most accessible of the RGB LED TVs we’ve seen thus far. Calling the new models proof of “what’s scalable,” we’re hoping these TVs may wind up being somewhere in the premium range of current OLED and QLED tech, though pricing is still up in the air. While specs are limited so far, Hisense says these models will provide “dramatically expanded color range with richer saturation and more accurate tonal reproduction than standard premium TVs on the market.” I can’t wait to see if they deliver.

It’s still early days for RGB displays, and way too soon to count out other new display technologies or OLED, which continues to evolve further than we ever expected. We’ll find out a lot more this year, but what’s clear is that the future of TV is brighter, cheaper, and better-looking than ever.

“On the matchmaking app, if we ask you a question and your tonality changes in the response, it cues to us that you may not be telling us the full truth. And so we’ll ask you that same question in two or three different ways throughout your experience,” Cohen-Aslatei says. “We built this to mimic what a matchmaker would do for a client. The LLM is tracking pitch and tone change in your voice because we want to make sure that we have an accurate understanding of who you are and what you’re looking for.”

After answering dozens more questions about lifestyle, future goals, boundaries, family, attraction, hobbies, and more over the course of a few days, Tai told me it’d take the information provided and get back to me. Two days later, I received my first two potential matches.

I Love You, Alive Girl

As a 31-year-old woman, I put my ideal age range at a healthy 26 to 40 years old. My first two matches were 23 and 47. One was not alive when 9/11 happened, and the other had already graduated from college at that time. Off to a rocky start.

When a potential match is found, the person’s picture is blurred, and Tai gives you a synopsis of what makes you a potential good match. (You need to provide selfie verification to confirm identity, and no one unverified will ever be matched.) After that, you can click to see a bit more about them, like profession, age, income, and a short bio that the AI creates.

At this stage of AI adoption, there is still a strong statistical bias toward, let’s say, men who wear wraparound sunglasses and think driving a Cybertruck is a sign of virility. Nearly every one of the 16 matches I received during testing was Christian and wanting children ASAP, which Tai flagged each time as a potential issue. Many were also flagged initially by Tai because they only wanted to date a certain race or valued traditional gender roles, both of which I made clear that I wasn’t aligned with.

Out of journalistic duty, I accepted every match I received; even a MMA-loving body builder that enjoys grilling meat (I’m vegan) and going to the gun range (I’m generally anti-gun). Matches ranged from Portland, Oregon, to DC, to New York City (where I live, although most matches were outside NYC). Overall, not a single person I was matched with would be someone I’d swipe right on if I saw them on a traditional dating app.

If you accept, you’ll either need to wait for the other person to accept or pass on the match, or they will have already accepted, and you can begin chatting. Here, your AI dating coach steps in to play wingman, providing prompts based on the other person’s profile, highlighting similarities you have, and giving conversation questions based on answers from the match’s profile. Not only does the coach provide potential ice breakers (and responses), you can also chat and ask for pointers.

I asked it to give me tips on how to break the ice with new matches, and it gave me advice, with each point having an explanatory paragraph below. Advice included giving compliments, asking open-ended questions, using humor, referencing current events, sharing about yourself, and mentioning mutual interests. The advice was basic but solid, and mirrored what the coach was doing with the provided conversation prompts.

This is all a great idea in theory, and could be very helpful with people who have a tough time communicating with strangers. But it could also lead to a bigger problem. You don’t really know who you’ve been talking to if AI has been doing all of the chatting for you. And if you meet in person, you don’t know much about your date’s actual personality. You can tell so much from how people type, what questions they ask, and their sense of humor. That was all missing here.