As generative AI models grow in popularity, a team of MIT researchers has developed a novel auditing method to identify models that can produce child sexual abuse material without generating illegal content. This scalable technique examines modifications within a model's structure, achieving 100% accuracy in detection, enhancing child safety online.

The article explores the rise of neocloud companies, CoreWeave and Nebius, as they respond to skyrocketing demand for AI infrastructure fueled by major players like Microsoft and Meta. It examines their innovative financing structures, challenges related to profitability, and the implications of these partnerships on the future of cloud computing.

Igropyr is a web server that self-recovers from crashes and supports live code updates without downtime. It uses a worker pool model, allowing requests to be processed while maintaining system stability. This server architecture ensures consistent fault reporting and efficient transaction management, all while relying solely on Scheme programming.

This study examines the persistence of SARS-CoV-2 in heart tissues of deceased individuals with cardiac long COVID symptoms. Findings indicate a significant association between viral detection and cardiac issues, suggesting that lingering viral presence may contribute to ongoing health problems in these patients.

The article discusses the author's journey of reviving old Java applets for teaching mathematics using modern AI tools. After a successful migration to JavaScript, the author experiments with creating new interactive visualizations for complex concepts, including the Gilbreath conjecture and special relativity, while addressing potential coding issues.

This article explores a vulnerability found in the Motorola MR2600 router that allows for unauthenticated remote code execution. By misconfiguring authentication checks, attackers can upload malicious firmware, ultimately compromising the device. The findings highlight important security implications for users of the MR2600 and similar routers.

AI tools are boosting productivity among scientists, enabling them to publish more and gain visibility faster. However, this efficiency is leading to less diversity in research topics, as many follow similar, data-rich paths. The study highlights a troubling trend: individual success may be compromising broader scientific inquiry.

Harness engineering focuses on the essential scaffolding that supports AI coding agents, emphasizing how improvements to this structure can enhance performance. By addressing mistakes as learning opportunities, engineers can create effective configurations that optimize an agent's capabilities, proving that design often matters more than the underlying model.

A recent study by Google Research explores network-aware routing in navigation apps to enhance urban traffic efficiency. By coordinating just a small percentage of trips, cities can reduce congestion and emissions significantly. The research establishes a framework for future improvements in traffic management through cooperative routing strategies.

Ploy's agent has transitioned to using OpenAI's new GPT-5.6 Sol model, enhancing marketing website creation significantly. This upgrade promises faster build times, reduced costs, and improved design outputs, despite some initial challenges. A detailed evaluation reveals how the model outperforms its predecessor in various aspects of web development.

A comparison of Claude Code and OpenCode reveals significant differences in token usage and caching efficiency. While Claude Code generally consumes more tokens and incurs higher costs, it can be more efficient for complex tasks due to batching. The findings have implications for operating AI in production environments.

Open models in AI face increasing threats from regulatory actions aimed at limiting their capabilities, particularly from the U.S. government, amid fears of competition from Chinese counterparts. As discussions unfold, the balance between innovation and control becomes critical for the future of open-source AI.

GATS (Graph-Augmented Tree Search) enhances agent planning by integrating a layered world model, eliminating the need for large language model calls during inference. This framework outperforms existing methods like LATS and ReAct, achieving a 100% success rate in complex planning tasks while producing consistent and deterministic plans.

SafeExplorer introduces an unbiased policy gradient estimator to enhance reinforcement learning in physical robots, minimizing training falls. By refining proximal policy optimization, it significantly reduces falls during training while matching or exceeding final rewards across multiple environments, making it a promising alternative to traditional methods.

Long-Horizon-Terminal-Bench introduces a new benchmark for evaluating AI agents on complex, long-horizon tasks requiring iterative problem-solving. By breaking tasks into graded subtasks with dense rewards, this work highlights agent performance beyond final outcomes, revealing key insights and areas for improvement in AI capabilities.