We establish a theoretical link between adversarial training and operator norm regularization for deep neural networks.

This week, DeepSeek released an updated version of its R1 model on HuggingFace, reigniting the open-source versus closed-source competition. The updated version, called DeekSeek-R1-0528, has 685 billion parameters, an upgrade from January's version, which had 671 billion. Unlike OpenAI and Google's models, which are famously closed-source, DeepSeek's model weights are publicly available. According to the benchmarks, the R1-0528 update has improved reasoning and inference capabilities and is closing the gap with OpenAI's o3 and Google's Gemini 2.5 Pro. This is an example of a lightweight model that is less capable but also requires less computing power.

We thank the reviewers for their detailed comments. We are glad to see a generally positive assessment of our work. We will report larger-scale results in the final draft. Below, we address specific reviewer comments. W. For simplicity, assume a parameter-free aggregation function that sums over messages from neighbouring nodes.

While imitation learning requires access to high-quality data, offline reinforcement learning (RL) should, in principle, perform similarly or better with substantially lower data quality by using a value function. However, current results indicate that offline RL often performs worse than imitation learning, and it is often unclear what holds back the performance of offline RL. Motivated by this observation, we aim to understand the bottlenecks in current offline RL algorithms. While poor performance of offline RL is typically attributed to an imperfect value function, we ask: is the main bottleneck of offline RL indeed in learning the value function, or something else? To answer this question, we perform a systematic empirical study of (1) value learning, (2) policy extraction, and (3) policy generalization in offline RL problems, analyzing how these components affect performance.

Recent works have revealed the role of geometric partitions and Sperner's lemma (and its variations) in designing replicable learning algorithms and in establishing impossibility results.

When you first think of AI, you probably think of a text-based chatbot like ChatGPT or Google's Gemini. But AI tools are getting a whole lot more versatile. These days, even basic AI chatbots offer a range of media creation tools, including AI image generators. Some of the best AI image generators have been around for some time now. In the early days, they were inconsistent, struggled with realism, and often failed to properly follow instructions.

Meta Ray-Bans already provide plenty of utility for people with low vision, but the glasses are about to get even better. Also: I've tested the Meta Ray-Bans for months, and these 5 features still amaze me Meta has announced two new accessibility advancements for its smart glasses designed to help users navigate the world around them. The first feature is available now. Meta said users can get descriptive responses about what their glasses see. Meta explained that this feature is useful for anyone, especially those with vision impairments.

Imagine losing your ability to speak or move, yet still having so much to say. For Brad G. Smith, this became his reality after being diagnosed with ALS, a rare and progressive disease that attacks the nerves controlling voluntary muscle movement. But thanks to a groundbreaking Neuralink brain implant, Smith is now able to communicate with the world using only his thoughts. Join The FREE CyberGuy Report: Get my expert tech tips, critical security alerts and exclusive deals -- plus instant access to my free Ultimate Scam Survival Guide when you sign up! Before receiving the Neuralink implant, Smith relied on eye-tracking technology to communicate.

At the Google I/O 2025 event on May 20, Google announced the release of Veo 3, a new AI video generation model that makes 8-second videos. Within hours of its release, AI artists and filmmakers were showing off shockingly realistic videos. You may have even seen some of these videos in your social media feeds and not realized they were artificially generated. To be blunt: We've never seen anything like Veo 3 before. And it's only going to get better.

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