U.S. health insurance is complex, and inadequate understanding and limited access to justice have dire implications for the most vulnerable. Advances in natural language processing present an opportunity to support efficient, case-specific understanding, and to improve access to justice and healthcare. Yet existing corpora lack context necessary for assessing even simple cases. We collect and release a corpus of reputable legal and medical text related to U.S. health insurance. We also introduce an outcome prediction task for health insurance appeals designed to support regulatory and patient self-help applications, and release a labeled benchmark for our task, and models trained on it.

The advancement of large language models (LLMs) struggles with the scarcity of high-quality, diverse training data. To address this limitation, we propose LinkSyn, a novel knowledge point (KP) graph-based synthesis framework that enables flexible control over discipline and difficulty distributions while balancing KP coverage and popularity. LinkSyn extracts KPs from question-answering (QA) seed data and constructs a KP graph to synthesize diverse QA data from multiple seeds strongly linked by KPs and sampled from graph walks. Specifically, LinkSyn incorporates (1) a knowledge distribution value function to guide the adjustment of path sampling probability and balance KP coverage and popularity during graph walks; (2) diffusion-based synthesis via DeepSeek-R1 by leveraging multiple seeds with dense logical associations along each path; and (3) high-difficulty QA enhancement within given disciplines by flexible difficulty adjustments. By executing LinkSyn, we synthesize LinkQA, a diverse multi-disciplinary QA dataset with 50B tokens. Extensive experiments on Llama-3 8B demonstrate that continual pre-training with LinkQA yields an average improvement of 11.51% on MMLU and CMMLU, establishing new SOT A results. LinkQA consistently enhances performance across model size and initial FLOPs scales.

Breakthroughs, discoveries, and DIY tips sent every weekday. Illinois has become the first state to enact legislation banning the use of AI tools like ChatGPT for providing therapy. The bill, signed into law by Governor J.B. Pritzker last Friday, comes amid growing research showing an increase in people experimenting with AI for mental health as the country faces a shortage of access to professional therapy services. The Wellness and Oversight for Psychological Resources Act, officially called HB 1806, prohibits healthcare providers from using AI for therapy and psychotherapy services. Specifically, it prevents AI chatbots or other AI-powered tools from interacting directly with patients, making therapeutic decisions, or creating treatment plans.

Checking your drink for drugs no longer needs to feel like a science experiment. Scientists in South Korea have created a new solution, a temporary tattoo sticker that instantly detects tampering. This simple sticker works fast, stays discreet, and offers surprisingly powerful protection. At first glance, it looks like ordinary skin art. The sticker detects GHB (gamma hydroxybutyrate), a drug commonly used to spike drinks.

Arts, creative and media groups have demanded the government rule out allowing big tech companies to take Australian content to train their artificial intelligence models, with concerns such a shift would "sell out" Australian workers and lead to "rampant theft" of intellectual property. "It is not appropriate for big tech to steal the work of Australian artists, musicians, creators, news media, journalism, and use it for their own ends without paying for it," Ley said on Wednesday. In an interim report on "harnessing data and digital technology", the Productivity Commission set out proposals for how tech, including AI, could be regulated and treated in Australia, suggesting it could boost productivity by between 0.5% and 13% over the next decade, adding up to 116bn to Australia's GDP. The commission suggested several possible remedies, including expanding licensing schemes, or an exemption for "text and data mining" and expanding the existing fair dealing rules, which it said existed in other countries. The latter suggestion prompted fierce pushback from arts, creative and media companies, which raised alarm their work could be left open for massively wealthy tech companies to use – without compensation or payment – to train AI models.

A man who is bringing a High Court challenge against the Metropolitan Police after live facial recognition technology wrongly identified him as a suspect has described it as "stop and search on steroids". Shaun Thompson, 39, was stopped by police in February last year outside London Bridge Tube station. Privacy campaign group Big Brother Watch said the judicial review, due to be heard in January, was the first legal case of its kind against the "intrusive technology". The Met, which announced last week that it would double its live facial recognition technology (LFR) deployments, said it was removing hundreds of dangerous offenders and remained confident its use is lawful.

When Ming* found a hidden camera in her bedroom, she prayed for a reasonable explanation, wondering whether her boyfriend had placed it there to record memories of their "happy life" together. But hope quickly turned to horror. Ming's boyfriend had been secretly taking sexually exploitative photos of not just Ming and her female friends, but also of other women in other locations, then using AI technology to generate pornographic images of them. After Ming confronted him, he "begged for mercy" but became angry when she refused to forgive him, Ming reportedly told Chinese news outlet Jimu News. Ming is just one of many women in China who have been covertly photographed or filmed – both in private and public spaces, including toilets – by voyeurs who have then circulated or sold the images online without consent.

The increasing demand for data privacy, driven by regulations such as GDPR and CCPA, has made Machine Unlearning (MU) essential for removing the influence of specific training samples from machine learning models while preserving performance on retained data. However, most existing MU methods lack rigorous statistical guarantees, rely on heuristic metrics, and often require computationally expensive retraining baselines. To overcome these limitations, we introduce a new definition for MU based on Conformal Prediction (CP), providing statistically sound, uncertainty-aware guarantees without the need for the concept of naive retraining. We formalize conformal criteria that quantify how often forgotten samples are excluded from CP sets, and propose empirical metrics,the Efficiently Covered Frequency (ECF at c) and its complement, the Efficiently Uncovered Frequency (EuCF at d), to measure the effectiveness of unlearning. We further present a practical unlearning method designed to optimize these conformal metrics. Extensive experiments across diverse forgetting scenarios, datasets and models demonstrate the efficacy of our approach in removing targeted data.

Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their ability to forecast future events remains understudied. A year ago, large language models struggle to come close to the accuracy of a human crowd. I evaluate state-of-the-art LLMs on 464 forecasting questions from Metaculus, comparing their performance against top forecasters. Frontier models achieve Brier scores that ostensibly surpass the human crowd but still significantly underperform a group of experts.

The increasing adoption of web crawling opt-outs by copyright holders of online content raises critical questions about the impact of data compliance on large language model (LLM) performance. However, little is known about how these restrictions (and the resultant filtering of pretraining datasets) affect the capabilities of models trained using these corpora. In this work, we conceptualize this effect as the $\textit{data compliance gap}$ (DCG), which quantifies the performance difference between models trained on datasets that comply with web crawling opt-outs, and those that do not. We measure the data compliance gap in two settings: pretraining models from scratch and continual pretraining from existing compliant models (simulating a setting where copyrighted data could be integrated later in pretraining). Our experiments with 1.5B models show that, as of January 2025, compliance with web data opt-outs does not degrade general knowledge acquisition (close to 0\% DCG). However, in specialized domains such as biomedical research, excluding major publishers leads to performance declines. These findings suggest that while general-purpose LLMs can be trained to perform equally well using fully open data, performance in specialized domains may benefit from access to high-quality copyrighted sources later in training. Our study provides empirical insights into the long-debated trade-off between data compliance and downstream model performance, informing future discussions on AI training practices and policy decisions. Our website is available at https://data-compliance.github.io/.