WIRED goes on location--and on camera--with the cult hit. On March 16, 2026, at 5:45 pm in a leafy suburb of Atlanta called Sandy Springs, police pound on the door of a neglected French Country-style mansion, rifles at the ready, bodycams rolling. Minutes earlier, a distress call came from someone claiming to be hiding from a gunman in the mansion's downstairs bathroom. The dispatcher heard a gunshot ring out in the distance, then the line disconnected. "Open the door!" an officer yells. A calm young man with a mullet and woolly eyebrows steps out, hands raised. The police ask him who else is in the house. "Just my friends," he replies, as seven other young people, men and women, silently file out behind him, less evidently relaxed. They remain outside while two officers search the house. Inside the mansion there are no immediate signs of a massacre, but the decor alone arouses suspicion. All of the windows are frosted over, so only a chilly light leaks in. The place is a mess, and the walls are adorned with lurid, seemingly AI-generated art: a frowning baby holding an assault rifle, a rubber ducky bobbing in a mug of what looks like black coffee, a lidless and levitating eyeball crying into a martini glass. The rooms are painted primary colors, grass green and cherry red, like a kindergarten class. A vape dangles from a doorframe by a chain, suspended at mouth level. The pantry is practically empty. The bedroom is a dormitory featuring seven identical twin beds. No one is hiding in the bathroom. The call, it seems, was a prank. The police return to the driveway and ask, "What is it that you guys are doing here?" "We're just livestreaming," says a man in a camo hat named Matt. "You guys don't have any firearms or anything inside the house?" There are guns in the house, Matt says, for self-defense. Fans of their livestream can be obsessive, he explains, and tend to have perverse ideas about jokes. The officer asks to see their weapons, and they go downstairs. The room is cluttered with ergonomic swivel chairs, desks strewn with takeout containers and energy drinks, two flatscreen TVs, and a dozen computer monitors.

'Look Mum, one point': Why does the UK keep getting Eurovision wrong? The UK has self-destructed at Eurovision all over again. Look Mum No Computer, aka musician Sam Battle, got one solitary point, ending up in last place. It's the third time we've been at the bottom of the table since 2020. We've made the top 10 once since 2010.

Quantitative Relative Judgment Aggregation (QRJA) is a new research topic in (computational) social choice. In the QRJA model, agents provide judgments on the relative quality of different candidates, and the goal is to aggregate these judgments across all agents. In this work, our main conceptual contribution is to explore the interplay between QRJA in a social choice context and its application to ranking prediction. We observe that in QRJA, judges do not have to be people with subjective opinions; for example, a race can be viewed as a ``judgment'' on the contestants' relative abilities. This allows us to aggregate results from multiple races to evaluate the contestants' true qualities. At a technical level, we introduce new aggregation rules for QRJA and study their structural and computational properties. We evaluate the proposed methods on data from various real races and show that QRJA-based methods offer effective and interpretable ranking predictions.

Quantitative Relative Judgment Aggregation (QRJA) is a new research topic in (computational) social choice. In the QRJA model, agents provide judgments on the relative quality of different candidates, and the goal is to aggregate these judgments across allagents.

'Minecraft was my way in' The Traitors 2026 finalist Jade. 'Minecraft was my way in' The Traitors 2026 finalist Jade. 'I didn't have anything to prove': what Traitors finalist Jade Scott learned about survival from video games T he latest series of The Traitors, which ended last week on a nail-biting finale, featured some of the usual characters - from guileless extroverts to wannabe Columbos endlessly observing fellow contestants for the slightest flicker of treachery. But one faithful stood out for her quiet determination, despite a ceaseless onslaught of suspicion and accusation. That person was Jade Scott, and I wasn't at all surprised when, quite early on in the series, she revealed she was a keen gamer.

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Our obsession with deadly game shows--from "The Running Man" and "Squid Game" to MrBeast's real-life reënactments--reflects a shift in the national mood to something increasingly zero-sum. It seems we can't get enough of game shows in which the losers die. "The Hunger Games" became a multibillion-dollar media franchise over the past decade, with audiences returning to the theatre, time and time again, to watch adolescents try to kill one another in an enormous arena--a contest devised by the leaders of a society rife with inequality. Netflix's " Squid Game " followed four hundred and fifty-six desperate individuals into an underworld where they play lethal versions of children's games in the hope of winning a life-changing amount of money. Four weeks after its release, the show had become Netflix's most-watched series ever; to date, the first season has been viewed more than two hundred and sixty-five million times.

Taskmaster is a British television show that combines comedic performance with a formal scoring system. Despite the appearance of structured competition, it remains unclear whether scoring dynamics contribute meaningfully to audience engagement. We conducted a statistical analysis of 162 episodes across 18 series, using fifteen episode-level metrics to quantify rank volatility, point spread, lead changes, and winner dominance. None of these metrics showed a significant association with IMDb ratings, even after controlling for series effects. Long-term trends suggest that average points have increased over time, while volatility has slightly declined and rank spread has remained stable. These patterns indicate an attempt to enhance competitive visibility without altering the show's structural equilibrium. We also analyzed contestant rank trajectories and identified five recurring archetypes describing performance styles. These patterns suggest that viewer interest is shaped more by contestant behavior than by game mechanics.

We present AutoBench, a fully automated and self-sustaining framework for evaluating Large Language Models (LLMs) through reciprocal peer assessment. This paper provides a rigorous scientific validation of the AutoBench methodology, originally developed as an open-source project by eZecute S.R.L.. Unlike static benchmarks that suffer from test-set contamination and limited adaptability, AutoBench dynamically generates novel evaluation tasks while models alternately serve as question generators, contestants, and judges across diverse domains. An iterative weighting mechanism amplifies the influence of consistently reliable evaluators, aggregating peer judgments into consensus-based rankings that reflect collective model agreement. Our experiments demonstrate strong correlations with established benchmarks including MMLU-Pro and GPQA (respectively 78\% and 63\%), validating this peer-driven evaluation paradigm. The multi-judge design significantly outperforms single-judge baselines, confirming that distributed evaluation produces more robust and human-consistent assessments. AutoBench offers a scalable, contamination-resistant alternative to static benchmarks for the continuous evaluation of evolving language models.

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