High-stakes texts such as patent claims, medical records, and technical reports are structurally complex and demand a high degree of reliability and precision. While large language models (LLMs) have recently been applied to automate their generation in high-stakes domains, reliably evaluating such outputs remains a major challenge. Conventional natural language generation (NLG) metrics are effective for generic documents but fail to capture the structural and legal characteristics essential to evaluating complex high-stakes documents. To address this gap, we propose PatentScore, a multi-dimensional evaluation framework specifically designed for one of the most intricate and rigorous domains, patent claims. PatentScore integrates hierarchical decomposition of claim elements, validation patterns grounded in legal and technical standards, and scoring across structural, semantic, and legal dimensions. In experiments on our dataset which consists of 400 Claim1, PatentScore achieved the highest correlation with expert annotations ($r = 0.819$), significantly outperforming widely used NLG metrics. This work establishes a new standard for evaluating LLM-generated patent claims, providing a solid foundation for research on patent generation and validation.

Terror charges against Luigi Mangione were dismissed in Manhattan court while supporters gathered outside, including one claiming marriage to his AI.

CEO Sam Altman announced an age-prediction system and new parental controls in a blog post on Tuesday. OpenAI announced new teen safety features for ChatGPT on Tuesday as part of an ongoing effort to respond to concerns about how minors engage with chatbots . The company is building an age-prediction system that identifies if a user is under 18 years old and routes them to an " age-appropriate " system that blocks graphic sexual content. If the system detects that the user is considering suicide or self-harm, it will contact the user's parents. In cases of imminent danger, if a user's parents are unreachable, the system may contact the authorities.

In the indictment, prosecutors claim Tyler Robinson planned Kirk's killing in advance, citing rooftop surveillance, engraved bullets, and a written note as they seek the death penalty. A TV monitor displays a picture of Tyler Robinson, a suspect in the killing of Charlie Kirk in Orem, Utah. Utah County prosecutors on Tuesday charged Tyler Robinson in the shooting death of conservative activist Charlie Kirk at Utah Valley University, a murder officials say was politically motivated. They intend to seek the death penalty. Utah County Attorney Jeff Gray announced the indictment at a midday news conference, listing charges of aggravated murder, felony discharge of a firearm causing serious bodily injury, and commission of a violent offense in the presence of a child.

Grok's Responses Are Only Getting More Bizarre Listen to more stories on the Noa app. When video of Charlie Kirk's assassination began circulating on X last week, Elon Musk's chatbot described it in upbeat terms. As users sought information about Kirk's condition, the bot, Grok, declared to some of them that the horrific footage was satire. This is a "meme edit," Grok told one user; Kirk "takes the roast in stride with a laugh--he's faced tougher crowds," it told another. "Yes, he survives this one easily." In the past several months, Grok has been on quite the hot streak: The bot spread false information about a supposed " white genocide," called for a second Holocaust while annointing itself "MechaHitler," and provided me with a list of what it believes the "good races" are.

The Cloudflare CEO joined to talk about standing up to content scraping, the internet's potential futures, and his company's relationship to Trump. Matthew Prince may not be a household name, but the world most certainly knows his work. Prince is the cofounder and CEO of Cloudflare . Launched in 2010, the internet infrastructure company has found itself increasingly in the position of serving as the web's bodyguard. It filters out bad traffic, keeps sites safe, and stops them from crashing when too many people visit. Its tools defend against DDoS attacks. In 2017, Cloudflare made headlines when it dropped white supremacist site The Daily Stormer . Cloudflare's severing of ties with The Daily Stormer marked a momentous shift, one that came after years of claiming a neutral stance. Prince continues to evolve the way Cloudflare works. In July, the company rolled out a new tool tasked with blocking unauthorized AI scraping. It effectively creates a pay-per-crawl model requiring AI platforms to shell out money if they want access to a site's content. On this episode of, I talked to Prince about publishing, the old internet, and how his ideal version of the future web means that OpenAI just might become the Netflix of content. KATIE DRUMMOND: Good to have you here, Matthew. You should have been warned ahead of time, but you probably weren't.

The risks posed when kids form bonds with chatbots have turned AI safety from an abstract worry into a political flashpoint. As long as there has been AI, there have been people sounding alarms about what it might do to us: rogue superintelligence, mass unemployment, or environmental ruin from data center sprawl. But this week showed that another threat entirely--that of kids forming unhealthy bonds with AI--is the one pulling AI safety out of the academic fringe and into regulators' crosshairs. This has been bubbling for a while. Two high-profile lawsuits filed in the last year, against Character.AI and OpenAI, allege that companion-like behavior in their models contributed to the suicides of two teenagers. A study by US nonprofit Common Sense Media, published in July, found that 72% of teenagers have used AI for companionship.

A United Nations commission of inquiry says Israel has committed genocide against Palestinians in Gaza. A new report says there are reasonable grounds to conclude that four of the five genocidal acts defined under international law have been carried out since the start of the war with Hamas in 2023: killing members of a group, causing them serious bodily and mental harm, deliberately inflicting conditions calculated to destroy the group, and preventing births. It cites statements by Israeli leaders, and the pattern of conduct by Israeli forces, as evidence of genocidal intent. Israel's foreign ministry said it categorically rejected the report, denouncing it as distorted and false. A spokesperson accused the three experts on the commission of serving as Hamas proxies and relying entirely on Hamas falsehoods, laundered and repeated by others that had already been thoroughly debunked.

'I have to do it': Why one of the world's most brilliant AI scientists left the US for China In 2020, after spending half his life in the US, Song-Chun Zhu took a one-way ticket to China. By the time Song-Chun Zhu was six years old, he had encountered death more times than he could count. This was the early 1970s, the waning years of the Cultural Revolution, and his father ran a village supply store in rural China . There was little to do beyond till the fields and study Mao Zedong at home, and so the shop became a refuge where people could rest, recharge and share tales. Zhu grew up in that shop, absorbing a lifetime's worth of tragedies: a family friend lost in a car crash, a relative from an untreated illness, stories of suicide or starvation. "That was really tough," Zhu recalled recently. The young Zhu became obsessed with what people left behind after they died. One day, he came across a book that contained his family genealogy. When he asked the bookkeeper why it included his ancestors' dates of birth and death but nothing about their lives, the man told him matter of factly that they were peasants, so there was nothing worth recording. He resolved that his fate would be different. Today, at 56, Zhu is one of the world's leading authorities in artificial intelligence. In 1992, he left China for the US to pursue a PhD in computer science at Harvard. Later, at University of California, Los Angeles (UCLA), he led one of the most prolific AI research centres in the world, won numerous major awards, and attracted prestigious research grants from the Pentagon and the National Science Foundation. He was celebrated for his pioneering research into how machines can spot patterns in data, which helped lay the groundwork for modern AI systems such as ChatGPT and DeepSeek. He and his wife, and their two US-born daughters, lived in a hilltop home on Los Angeles's Mulholland Drive. He thought he would never leave. But in August 2020, after 28 years in the US, Zhu astonished his colleagues and friends by suddenly moving back to China, where he took up professorships at two top Beijing universities and a directorship in a state-sponsored AI institute.

We propose FACTORS, a framework that combines design of experiments with Shapley decomposition to address performance and stability issues that are sensitive to combinations of training factors. Our approach consistently estimates main effects and two-factor interactions, then integrates them into a risk-adjusted objective function that jointly accounts for uncertainty and cost, enabling reliable selection of configurations under a fixed budget. Effect estimation is implemented through two complementary paths: a plug-in path based on conditional means, and a least-squares path that reconstructs Shapley contributions from samples. These paths are designed to work complementarily even when design density and bias levels differ. By incorporating standardization of estimates, bias correction, and uncertainty quantification, our procedure ensures comparability across heterogeneous factor spaces and designs, while a lightweight search routine yields configurations within practical time even for large factor spaces. On the theoretical side, we provide error decompositions, sample complexity analysis, and upper bounds on optimality gaps. On the interpretive side, we summarize main effects and interactions in map form, highlighting adjustment priorities and safe improvement pathways. Across diverse datasets and design conditions, our approach improves rank preservation and optimal configuration identification, reduces decision-making risks, and offers a tuning foundation that delivers interpretable justification alongside stable performance gains even under budget constraints.