One chilly day in November 2025, community worker Mike Prado drove through Abilene, Tex., handing out blankets, socks, and jackets to unhoused individuals across the city. People sat on curbs, alleyway after alleyway, their meager belongings soaked by the previous night's hard rain. Prado has worked in this community for a decade, and was once homeless in Abilene himself. Prado has witnessed difficult years--but the current situation was the worst he'd ever seen, he told TIME. One man with a walker approached Prado outside of the Hope Haven offices--an Abilene nonprofit where Prado works, which operates a shelter and helps people with vouchers find housing--and accepted a jacket from him.

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OpenAI CEO Sam Altman scrambles to boost ChatGPT's quality and first lady Melania Trump rolls out an AI-powered Spanish translation of her memoir.

Senator Ted Cruz leads efforts to pass AI amnesty through the NDAA, giving Big Tech federal preemption without rules to protect conservatives and children.

'It's going much too fast': the inside story of the race to create the ultimate AI On the 8.49am train through Silicon Valley, the tables are packed with young people glued to laptops, earbuds in, rattling out code. As the northern California hills scroll past, instructions flash up on screens from bosses: fix this bug; add new script. There is no time to enjoy the view. These commuters are foot soldiers in the global race towards artificial general intelligence - when AI systems become as or more capable than highly qualified humans. Here in the Bay Area of San Francisco, some of the world's biggest companies are fighting it out to gain some kind of an advantage. And, in turn, they are competing with China. This race to seize control of a technology that could reshape the world is being fuelled by bets in the trillions of dollars by the US's most powerful capitalists. Passengers get off a train at Palo Alto station.

Autonomous driving heavily relies on accurate and robust spatial perception. Many failures arise from inaccuracies and instability, especially in long-tail scenarios and complex interactions. However, current vision-language models are weak at spatial grounding and understanding, and VLA systems built on them therefore show limited perception and localization ability. To address these challenges, we introduce Percept-WAM, a perception-enhanced World-Awareness-Action Model that is the first to implicitly integrate 2D/3D scene understanding abilities within a single vision-language model (VLM). Instead of relying on QA-style spatial reasoning, Percept-WAM unifies 2D/3D perception tasks into World-PV and World-BEV tokens, which encode both spatial coordinates and confidence. We propose a grid-conditioned prediction mechanism for dense object perception, incorporating IoU-aware scoring and parallel autoregressive decoding, improving stability in long-tail, far-range, and small-object scenarios. Additionally, Percept-WAM leverages pretrained VLM parameters to retain general intelligence (e.g., logical reasoning) and can output perception results and trajectory control outputs directly. Experiments show that Percept-WAM matches or surpasses classical detectors and segmenters on downstream perception benchmarks, achieving 51.7/58.9 mAP on COCO 2D detection and nuScenes BEV 3D detection. When integrated with trajectory decoders, it further improves planning performance on nuScenes and NAVSIM, e.g., surpassing DiffusionDrive by 2.1 in PMDS on NAVSIM. Qualitative results further highlight its strong open-vocabulary and long-tail generalization.

In this episode of Uncanny Valley, we discuss our scoop about how the Department of Homeland Security illegally collected Chicago residents' data for month, as well as the news of the week. In today's episode, host Zoë Schiffer is joined by executive editor Brian Barrett to discuss five stories you need to know about this week--from how AI affairs can now be grounds for divorce, to why Google is suing one of the largest networks of text scammers. Then, we dive into how the Department of Homeland Security illegally gathered the data of hundreds of Chicago residents. If the US Has to Build Data Centers, Here's Where They Should Go This Is the Platform Google Claims Is Behind a'Staggering' Scam Text Operation AI Relationships Are on the Rise. Please help us improve Uncanny Valley by filling out our listener survey. Write to us at uncannyvalley@wired.com. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link. Note: This is an automated transcript, which may contain errors. Today on the show, we're bringing you five stories that you need to know about this week, including our scoop about how the Department of Homeland Security, or DHS, collected Chicago residents' data for months in violation of domestic espionage rules. I'm joined today by WIRED's executive editor Brian Barrett.

The broad capabilities and substantial resources required to train Large Language Models (LLMs) make them valuable intellectual property, yet they remain vulnerable to copyright infringement, such as unauthorized use and model theft. LLM fingerprinting, a non-intrusive technique that compares the distinctive features (i.e., fingerprint) of LLMs to identify whether an LLM is derived from another, offers a promising solution to copyright auditing. However, its reliability remains uncertain due to the prevalence of diverse model modifications and the lack of standardized evaluation. In this SoK, we present the first comprehensive study of the emerging LLM fingerprinting. We introduce a unified framework and taxonomy that structures the field: white-box methods are classified based on their feature source as static, forward-pass, or backward-pass fingerprinting, while black-box methods are distinguished by their query strategy as either untargeted or targeted. Furthermore, we propose LeaFBench, the first systematic benchmark for evaluating LLM fingerprinting under realistic deployment scenarios. Built upon 7 mainstream foundation models and comprising 149 distinct model instances, LeaFBench integrates 13 representative post-development techniques, spanning both parameter-altering methods (e.g., fine-tuning, quantization) and parameter-independent techniques (e.g., system prompts, RAG). Extensive experiments on LeaFBench reveal the strengths and weaknesses of existing methods, thereby outlining future research directions and critical open problems in this emerging field. The code is available at https://github.com/shaoshuo-ss/LeaFBench.

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