Kids everywhere know her voice--if not her name. WIRED talks to the former SM trainee about her rise to global superstardom with her hit song "Golden." EJAE, the voice and the writing talent behind "Golden," has gone platinum. The night before our interview, the 33-year-old singer-songwriter found out that record sales from the soundtrack had surged past a million units. Jimmy Fallon, of all people, delivered the news alongside a glimmering framed record when she was appearing on with Audrey Nuna and Rei Ami for the first full live performance of "Golden." Together the trio make up the singing voices of girl group -Huntr/x in Netflix's animated musical turned bona fide phenomenon. If you have a kid, you probably don't need a refresher, but the movie follows Huntr/x's Rumi, Mira, and Zoey as they juggle being astronomically famous while moonlighting as demon hunters. That Fallon appearance, and the appearance that predated it, might have been the first times that American audiences actually saw (and heard) the human being behind that inescapable song.

What it's like to be in the middle of a conspiracy theory (according to a conspiracy theory expert) Mike Rothschild has spent years studying the rise of QAnon and antivaccine conspiracism. After his house in Altadena, California, burned down, he found himself mired in similarly sticky webs of misinformation. On a gloomy Saturday morning this past May, a few months after entire blocks of Altadena, California, were destroyed by wildfires, several dozen survivors met at a local church to vent their built-up frustration, anger, blame, and anguish. As I sat there listening to one horror story after another, I almost felt sorry for the very polite consultants who were being paid to sit there, and who couldn't do a thing about what they were hearing. Hosted by a third-party arbiter at the behest of Los Angeles County, the gathering was a listening session in which survivors could "share their experiences with emergency alerts and evacuations" for a report on how the response to the Eaton Fire months earlier had succeeded and failed. It didn't take long to see just how much failure there had been. After a small fire started in the bone-dry brush of Pasadena's Eaton Canyon early in the evening of Tuesday, January 7, 2025, the raging Santa Ana winds blew its embers into nearby Altadena, the historically Black and middle-class town just to the north. By Wednesday morning, much of it was burning.

Why do so many people think the Fruit of the Loom logo had a cornucopia? And while some people may laugh and move on, others spend years searching for an explanation. There is a shirt currently listed on eBay for $2,128.79. It was not designed by Versace or Dior, nor spun from the world's finest silk. In fact, a tag proudly declares, "100% cotton made in Myanmar"--but it's a second tag, just below that one, that makes this blue button-down so expensive. "I looked at it and I was like,," says Brooke Hermann, the 30-year-old Kentucky-based reseller who bought the top for $1 at a secondhand sale in 2024. "This doesn't look like any other Fruit of the Loom tag I've ever seen." Quick question: Does the Fruit of the Loom logo feature a cornucopia? Many of us have been wearing the casualwear company's T-shirts and underpants for decades, and yet the question of whether there is a woven brown horn of plenty on the logo is surprisingly contentious. According to a 2022 poll by the research company YouGov, 55% of Americans believe the logo does include a cornucopia, 25% are unsure, and only 21% are confident that it doesn't, even though this last group is correct.

Google sets a new standard with a full IP68 rating for foldable phones. Google sets a new standard with a full IP68 rating for foldable phones. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. G oogle's third-generation folding phone promises to be more durable than all others as the first with full water and dust resistance while also packing lots of advanced AI and an adaptable set of cameras.

The rapid adoption of Large Language Models (LLMs) has raised important concerns about the factual reliability of their outputs, particularly in low-resource languages such as Urdu. Existing automated fact-checking systems are predominantly developed for English, leaving a significant gap for the more than 200 million Urdu speakers worldwide. In this work, we present UrduFactBench and UrduFactQA, two novel hand-annotated benchmarks designed to enable fact-checking and factual consistency evaluation in Urdu. While UrduFactBench focuses on claim verification, UrduFactQA targets the factuality of LLMs in question answering. These resources, the first of their kind for Urdu, were developed through a multi-stage annotation process involving native Urdu speakers. To complement these benchmarks, we introduce UrduFactCheck, a modular fact-checking framework that incorporates both monolingual and translation-based evidence retrieval strategies to mitigate the scarcity of high-quality Urdu evidence. Leveraging these resources, we conduct an extensive evaluation of twelve LLMs and demonstrate that translation-augmented pipelines consistently enhance performance compared to monolingual ones. Our findings reveal persistent challenges for open-source LLMs in Urdu and underscore the importance of developing targeted resources. All code and data are publicly available at https://github.com/mbzuai-nlp/UrduFactCheck.

Recently, instruction-based image editing (IIE) has received widespread attention. In practice, IIE often modifies only specific regions of an image, while the remaining areas largely remain unchanged. Although these two types of regions differ significantly in generation difficulty and computational redundancy, existing IIE models do not account for this distinction, instead applying a uniform generation process across the entire image. This motivates us to propose RegionE, an adaptive, region-aware generation framework that accelerates IIE tasks without additional training. Specifically, the RegionE framework consists of three main components: 1) Adaptive Region Partition. We observed that the trajectory of unedited regions is straight, allowing for multi-step denoised predictions to be inferred in a single step. Therefore, in the early denoising stages, we partition the image into edited and unedited regions based on the difference between the final estimated result and the reference image. 2) Region-Aware Generation. After distinguishing the regions, we replace multi-step denoising with one-step prediction for unedited areas. For edited regions, the trajectory is curved, requiring local iterative denoising. To improve the efficiency and quality of local iterative generation, we propose the Region-Instruction KV Cache, which reduces computational cost while incorporating global information. 3) Adaptive Velocity Decay Cache. Observing that adjacent timesteps in edited regions exhibit strong velocity similarity, we further propose an adaptive velocity decay cache to accelerate the local denoising process. We applied RegionE to state-of-the-art IIE base models, including Step1X-Edit, FLUX.1 Kontext, and Qwen-Image-Edit. RegionE achieved acceleration factors of 2.57, 2.41, and 2.06. Evaluations by GPT-4o confirmed that semantic and perceptual fidelity were well preserved.

Audio Descriptions (ADs) convey essential on-screen information, allowing visually impaired audiences to follow videos. To be effective, ADs must form a coherent sequence that helps listeners to visualise the unfolding scene, rather than describing isolated moments. However, most automatic methods generate each AD independently, often resulting in repetitive, incoherent descriptions. To address this, we propose a training-free method, CoherentAD, that first generates multiple candidate descriptions for each AD time interval, and then performs auto-regressive selection across the sequence to form a coherent and informative narrative. To evaluate AD sequences holistically, we introduce a sequence-level metric, StoryRecall, which measures how well the predicted ADs convey the ground truth narrative, alongside repetition metrics that capture the redundancy across consecutive AD outputs. Our method produces coherent AD sequences with enhanced narrative understanding, outperforming prior approaches that rely on independent generations.

Autonomous agents powered by large language models (LLMs) have shown impressive capabilities in tool manipulation for complex task-solving. However, existing paradigms such as ReAct rely on sequential reasoning and execution, failing to exploit the inherent parallelism among independent sub-tasks. This sequential bottleneck leads to inefficient tool utilization and suboptimal performance in multi-step reasoning scenarios. We introduce Graph-based Agent Planning (GAP), a novel framework that explicitly models inter-task dependencies through graph-based planning to enable adaptive parallel and serial tool execution. Our approach trains agent foundation models to decompose complex tasks into dependency-aware sub-task graphs, autonomously determining which tools can be executed in parallel and which must follow sequential dependencies. This dependency-aware orchestration achieves substantial improvements in both execution efficiency and task accuracy. To train GAP, we construct a high-quality dataset of graph-based planning traces derived from the Multi-Hop Question Answering (MHQA) benchmark. We employ a two-stage training strategy: supervised fine-tuning (SFT) on the curated dataset, followed by reinforcement learning (RL) with a correctness-based reward function on strategically sampled queries where tool-based reasoning provides maximum value. Experimental results on MHQA datasets demonstrate that GAP significantly outperforms traditional ReAct baselines, particularly on multi-step retrieval tasks, while achieving dramatic improvements in tool invocation efficiency through intelligent parallelization. The project page is available at: https://github.com/WJQ7777/Graph-Agent-Planning.

Achieving high-fidelity, compact RGBD imaging presents a dual challenge: conventional compact optics struggle with RGB sharpness across the entire depth-of-field, while software-only Monocular Depth Estimation (MDE) is an ill-posed problem reliant on unreliable semantic priors. While deep optics with elements like DOEs can encode depth, they introduce trade-offs in fabrication complexity and chromatic aberrations, compromising simplicity. To address this, we first introduce a novel bio-inspired all-spherical monocentric lens, around which we build the Bionic Monocentric Imaging (BMI) framework, a holistic co-design. This optical design naturally encodes depth into its depth-varying Point Spread Functions (PSFs) without requiring complex diffractive or freeform elements. We establish a rigorous physically-based forward model to generate a synthetic dataset by precisely simulating the optical degradation process. This simulation pipeline is co-designed with a dual-head, multi-scale reconstruction network that employs a shared encoder to jointly recover a high-fidelity All-in-Focus (AiF) image and a precise depth map from a single coded capture. Extensive experiments validate the state-of-the-art performance of the proposed framework. In depth estimation, the method attains an Abs Rel of 0.026 and an RMSE of 0.130, markedly outperforming leading software-only approaches and other deep optics systems. For image restoration, the system achieves an SSIM of 0.960 and a perceptual LPIPS score of 0.082, thereby confirming a superior balance between image fidelity and depth accuracy. This study illustrates that the integration of bio-inspired, fully spherical optics with a joint reconstruction algorithm constitutes an effective strategy for addressing the intrinsic challenges in high-performance compact RGBD imaging. Source code will be publicly available at https://github.com/ZongxiYu-ZJU/BMI.

Accurate calibration of a Stewart platform is important for their precise and efficient operation. However, the calibration of these platforms using forward kinematics is a challenge for researchers because forward kinematics normally generates multiple feasible and unfeasible solutions for any pose of the moving platform. The complex kinematic relations among the six actuator paths connecting the fixed base to the moving platform further compound the difficulty in establishing a straightforward and efficient calibration method. The authors developed a new forward kinematics-based calibration method using Denavit-Hartenberg convention and used the Stewart platform Tiger 66.1 developed in their lab for experimenting with the photogrammetry-based calibration strategies described in this paper. This system became operational upon completion of construction, marking its inaugural use. The authors used their calibration model for estimating the errors in the system and adopted three compensation options or strategies as per Least Square method to improve the accuracy of the system. These strategies leveraged a high-resolution digital camera and off-the-shelf software to capture the poses of the moving platform's center. This process is non-invasive and does not need any additional equipment to be attached to the hexapod or any alteration of the hexapod hardware. This photogrammetry-based calibration process involves multiple high-resolution images from different angles to measure the position and orientation of the platform center in the three-dimensional space. The Target poses and Actual poses are then compared, and the error compensations are estimated using the Least-Squared methods to calculate the Predicted poses. Results from each of the three compensation approaches demonstrated noticeable enhancements in platform pose accuracies, suggesting room for further improvements.