Claude also went down for many users earlier today. If you had trouble using ChatGPT today, you aren't alone. The AI chatbot experienced a partial outage for many users this afternoon, with Down Detector saw reports reaching more than 12,000 reports around the peak point of the issue today.. OpenAI issued a status update shortly after noting that elevated error rates were occurring for ChatGPT and Platform users. That problem was marked as resolved at 5:14PM ET. While the initial outage may be repaired, OpenAI does still have an active status alert up. But the end may also be in sight for that final issue, because the current statement from the company is We have applied the mitigation and are monitoring the recovering.

We are working on implementing a mitigation, OpenAI said in a status update. If you've had trouble using ChatGPT today, you aren't alone. The AI chatbot is experiencing a partial outage for many users this afternoon. Down Detector reports of issues with the service leapt from almost nothing to more than 12,000 around 3PM ET. OpenAI issued a status update noting that elevated error rates are occurring for ChatGPT and Platform users.

Xcode 26.3 brings more robust support for programming agents like Claude. Apple has just released Xcode 26.3, and it's a big step forward in terms of the company's support of coding agents. The new release expands on the AI features the company introduced with Xcode 26 at WWDC 2025 to give systems like Claude and ChatGPT more robust access to its in-house IDE. With the update, Apple says Claude and OpenAI's Codex can search documentation, explore file structures, update project settings, and verify their work visually by capturing Xcode Previews and iterating through builds and fixes. This is in contrast to earlier releases of Xcode 26 where those same agents were limited in what they could see of a developer's Xcode environment, restricting their utility.

Using LLMs to talk with your friends is efficient. My friend recently attended a funeral, and midway through the eulogy, he became convinced that it had been written by AI. There was the telltale proliferation of abstract nouns, a surfeit of assertions that the deceased was "not just --he was " coupled with a lack of concrete anecdotes, and more appearances of the word than you would expect from a rec-league hockey teammate. It was both too good, in terms of being grammatically correct, and not good enough, in terms of being particular. My friend had no definitive proof that he was listening to AI, but his position--and I agree with him--is that when you know, you know. His sense was that he had just heard a computer save a man from thinking about his dead friend.

The showrunner thinks AI will be good for burgeoning filmmakers, but not for Hollywood blockbusters. Jonathan Nolan saw this coming. As a screenwriter, he's worked on several of his brother Christopher Nolan's films, from to the movies. Partnered with his wife Lisa Joy, he created HBO's and executive produced Amazon Prime's . But before that, he cut his TV teeth creating, a CBS procedural about a solitary tech billionaire who creates a piece of surveillance software aimed at stopping crime before it happens. It was fiction, but it's hard not to feel its prescience. With, now in its second season, Nolan also has his sights on the future. Based on the video game series of the same name, it's about a postapocalyptic America where everyone must survive in any way they can. So, what does Nolan see happening in the coming decades? For one, he doesn't think AI is going to replace human filmmakers. In fact, he thinks it could help aspiring directors get a foot in the door. He'd also like to see the demise of (most) social media--but understands that may never happen. For this week's episode of The Big Interview podcast, I asked Nolan about all of those things and more. Below you'll find his thoughts on writing Batman movies, classic cars, and what he'd actually bring to his own doomsday bunker. Thank you for having me. I'm delighted to have you here in person in New York. I'm from Canada so my barometer is a little off, but I tend to think of New York as wimpy cold. No, no, this is real. The older I get the weaker and more frail. So I can't tolerate [it]. I've been in LA for 25 years.

'She Has a Presence': The Superfans Who Turned Up for Opening Weekend WIRED attended two documentary screening parties--one on each coast--for the First Lady's film. For decades now, people have been wondering: Who is Melania Trump? The First Lady opens her 2024 memoir with a story about leaving her family in Slovenia to immigrate to America as a 26-year-old model. Ten years later, she became an American citizen. "It was not an easy process," she writes. "And my personal experience dealing with the trials of the immigration process opened my eyes to the difficulties faced by all who wish to become US citizens." OK, but what does that mean, exactly? Her husband, in both his terms as president, put harshly enforcing immigration policy at the center of his domestic agenda. This is all to say that I was authentically excited to see, the documentary that Amazon paid $40 million to acquire and $35 million to market. The director, Brett Ratner, previously accused of sexual misconduct by six different women, is currently in the news thanks to his appearance in a photo included in the most recent dump of Epstein files. What is Melania like behind closed doors?

The international AI safety report warns systems are improving rapidly - but remain prone to'hallucinations' and hard to control. The international AI safety report warns systems are improving rapidly - but remain prone to'hallucinations' and hard to control. The International AI Safety report is an annual survey of technological progress and the risks it is creating across multiple areas, from deepfakes to the jobs market. Commissioned at the 2023 global AI safety summit, it is chaired by the Canadian computer scientist Yoshua Bengio, who describes the "daunting challenges" posed by rapid developments in the field. The report is also guided by senior advisers, including Nobel laureates Geoffrey Hinton and Daron Acemoglu.

Large language models (LLMs) generate outputs by utilizing extensive context, which often includes redundant information from prompts, retrieved passages, and interaction history. In critical applications, it is vital to identify which context elements actually influence the output, as standard explanation methods struggle with redundancy and overlapping context. Minor changes in input can lead to unpredictable shifts in attribution scores, undermining interpretability and raising concerns about risks like prompt injection. This work addresses the challenge of distinguishing essential context elements from correlated ones. We introduce RISE (Redundancy-Insensitive Scoring of Explanation), a method that quantifies the unique influence of each input relative to others, minimizing the impact of redundancies and providing clearer, stable attributions. Experiments demonstrate that RISE offers more robust explanations than traditional methods, emphasizing the importance of conditional information for trustworthy LLM explanations and monitoring.

Self-attention has greatly contributed to the success of the widely used Transformer architecture by enabling learning from data with long-range dependencies. In an effort to improve performance, a gated attention model that leverages a gating mechanism within the multi-head self-attention has recently been proposed as a promising alternative. Gated attention has been empirically demonstrated to increase the expressiveness of low-rank mapping in standard attention and even to eliminate the attention sink phenomenon. Despite its efficacy, a clear theoretical understanding of gated attention's benefits remains lacking in the literature. To close this gap, we rigorously show that each entry in a gated attention matrix or a multi-head self-attention matrix can be written as a hierarchical mixture of experts. By recasting learning as an expert estimation problem, we demonstrate that gated attention is more sample-efficient than multi-head self-attention. In particular, while the former needs only a polynomial number of data points to estimate an expert, the latter requires exponentially many data points to achieve the same estimation error. Furthermore, our analysis also provides a theoretical justification for why gated attention yields higher performance when a gate is placed at the output of the scaled dot product attention or the value map rather than at other positions in the multi-head self-attention architecture.

Aligning large language models (LLMs) to diverse human preferences is fundamentally challenging since criteria can often conflict with each other. Inference-time alignment methods have recently gained popularity as they allow LLMs to be aligned to multiple criteria via different alignment algorithms at inference time. However, inference-time alignment is computationally expensive since it often requires multiple forward passes of the base model. In this work, we propose inference-aware meta-alignment (IAMA), a novel approach that enables LLMs to be aligned to multiple criteria with limited computational budget at inference time. IAMA trains a base model such that it can be effectively aligned to multiple tasks via different inference-time alignment algorithms. To solve the non-linear optimization problems involved in IAMA, we propose non-linear GRPO, which provably converges to the optimal solution in the space of probability measures.