Qatar has become the place where FIFA experiments with the next generation of football technology. The results are already visible across this year's World Cup. To casual soccer viewers, the game may look like it always has--same green field, 22 players, a referee, and the familiar rhythm of play unfolding over 90 minutes. The changes are only visible if you look beneath the familiar surface. What appears to be a traditional match is now supported by layers of tracking systems, automated analysis, and real-time data that run quietly in the background.

Seekers then come and try to find the hiders and, this being an online video game, shoot them. It's frantic, silly and fiendishly creative: finding a spot on one of the maps that you feel confident to paint yourself into - whether it's a laundry room or a farm outbuilding - is a challenging artistic and perceptual task, as well as a neat game mechanic. Meccha Chameleon perfectly encapsulates two popular and interconnected indie genres - prop games (hide and seek, but people disguise themselves as everyday objects) and the slightly pejoratively named "friendslop" (accessible, crudely designed multiplayer titles). So no wonder it has sold 7m units in less than a month.

From fake tickets to cloned websites, AI is magnifying World Cup scams. Can fans distinguish between what's real and what's not? You got a World Cup ticket. It arrived in your inbox with a QR code, professional branding, and a confirmation email that looked like the real thing. For years, spotting a scam was relatively simple.

The biggest World Cup ever is pushing fans, players, and host cities to their limits--and experts say this is only the beginning. It's often said that bigger means better. This year's FIFA World Cup may put that to the test. By almost any metric, the 2026 tournament is the largest ever: the most host countries; the longest distances between stadiums; the most players, teams, and matches; and then there's the eye-watering ticket prices . The scale is a logistical nightmare for fans, teams, and host cities. Held across three countries-- Canada, Mexico, and the US--48 teams (up from the usual 32) will navigate 16 host cities separated by thousands of miles and four distinct time zones.

The first ever halftime show at a FIFA World Cup final has been curated by Coldplay's Chris Martin and will feature performances from global superstars.

It might not feel all that different from older World Cups--for better or worse. Waymo, the Alphabet subsidiary offering robotaxi rides in 11 US metros right now, says it's ready for the FIFA World Cup . Match attendees can catch driverless rides to six of the 16 North American venues: stadiums in Atlanta, Houston, Los Angeles, Miami, and the San Francisco Bay Area. The sprawling football event, expected to attract some 6.5 million visitors to the continent over more than a month, could prove an exciting close-up for Waymo . The company says it's serving half-a-million paid rides a week--paltry stuff compared to the likes of ride-hail giants Uber and Lyft, but more impressive once you remember that the things don't have drivers.

The launch of "Xoli" adds to the technological efforts promoted by the federal government to turn the 2026 World Cup into an engine of development for the entire country. Xoli, the new chatbot, is named after the axolotl, a salamander with external gills. The Government of Mexico City has launched Xoli, a chatbot that will provide information on services, tourism, and cultural offerings. The platform was designed to meet the demand of the millions of visitors expected to arrive during the 2026 FIFA World Cup . However, the authorities assure that the tool will remain active once the sporting event is over, with the aim of promoting economic activities and facilitating access to public services in the capital.

Mexico's consumer protection agency and FIFA are working on a "ticket relocation system" that will allow those with extra World Cup tickets to sell them safely and at appropriate prices. The Mexican government has presented its strategy to turn this summer's World Cup soccer tournament into an engine to strengthen trade, sports, tourism, and culture in the country where most of the games will be hosted. The Mexico 2026 Social World Cup project includes cultural events like soccer matches between robots, a public transit plan, and a new app where fans can sell securely sell any tickets they can't use. During a conference last week, Mexican President Claudia Sheinbaum stated that the intention is "to leave a sporting legacy in our country that goes beyond the competition itself." "[In this World Cup ] the eyes of the world will be here," Sheinbaum said, "and what they will see is a great country with an enormous cultural heritage. They will see that we are building a nation that is fairer, freer, and more democratic."

The memorization of training data in large language models (LLMs) poses significant privacy and copyright concerns. Existing data extraction methods, particularly heuristic-based divergence attacks, often exhibit limited success and offer limited insight into the fundamental drivers of memorization leakage. This paper introduces Confusion-Inducing Attacks (CIA), a principled framework for extracting memorized data by systematically maximizing model uncertainty. We empirically demonstrate that the emission of memorized text during divergence is preceded by a sustained spike in token-level prediction entropy. CIA leverages this insight by optimizing input snippets to deliberately induce this consecutive high-entropy state. For aligned LLMs, we further propose Mismatched Supervised Fine-tuning (SFT) to simultaneously weaken their alignment and induce targeted confusion, thereby increasing susceptibility to our attacks. Experiments on various unaligned and aligned LLMs demonstrate that our proposed attacks outperform existing baselines in extracting verbatim and near-verbatim training data without requiring prior knowledge of the training data. Our findings highlight persistent memorization risks across various LLMs and offer a more systematic method for assessing these vulnerabilities.

LMMs have shown impressive visual understanding capabilities, with the potential to be applied in agents, which demand strong reasoning and planning abilities. Nevertheless, existing benchmarks mostly assess their reasoning abilities in language part, where the chain-of-thought is entirely composed of text.We consider the scenario where visual signals are continuously updated and required along the decision making process. Such vision-in-the-chain reasoning paradigm is more aligned with the needs of multimodal agents, while being rarely evaluated. In this paper, we introduce MageBench, a reasoning capability oriented multimodal agent benchmark that, while having light-weight environments, poses significant reasoning challenges and holds substantial practical value. This benchmark currently includes three types of environments: WebUI, Sokoban, and Football, comprising a total of 483 different scenarios. It thoroughly validates the agent's knowledge and engineering capabilities, visual intelligence, and interaction skills. The results show that only a few product-level models are better than random acting, and all of them are far inferior to human-level. More specifically, we found current models severely lack the ability to modify their planning based on visual feedback, as well as visual imagination, interleaved image-text long context handling, and other abilities. We hope that our work will provide optimization directions for LMM from the perspective of being an agent. We release our code and data at https://github.com/microsoft/MageBench.