Brief

Malaysia's communications regulator has issued a formal demand to TikTok, seeking an explanation for the platform's failure to remove a fake account that allegedly used AI to create offensive content targeting the country's king. The account posted false claims and manipulated images, including AI-generated videos, which the Malaysian Communications and Multimedia Commission (MCMC) deemed "grossly offensive, false, menacing and insulting." The MCMC is demanding immediate remedial actions and improved content moderation from TikTok, citing potential breaches of Malaysian law. AI

Palantir is in a dispute with the Pentagon's Defense Intelligence Agency (DIA) over its ability to bid on a contract to update the agency's data analytics system. The company argues that the DIA is wasting taxpayer funds by attempting to build a system from scratch, rather than considering commercial solutions like Palantir's. A White House official indicated support for fair competition among private sector technology providers, suggesting potential intervention to ensure Palantir and others can compete. AI

Databricks has launched Genie for Decarbonization Intelligence, a new tool designed to help energy sector companies bridge the gap between ESG reporting and actual decarbonization decisions. The platform allows sustainability leaders to query complex emissions and operational data using natural language, providing instant answers to inform forward-looking strategies. This aims to transform sustainability from a compliance burden into a competitive advantage by enabling data-driven decision-making. AI

COROS, a wearable technology company, is integrating its platform with large language models (LLMs) to analyze athlete training data. This new integration, called the COROS Training Hub (CTH), aims to provide deeper insights into performance and recovery by leveraging AI. The company is making this data available to LLMs like ChatGPT, allowing for more sophisticated analysis than previously possible. AI

Anthropic has released version 2.1.139 of its Claude Code tool, introducing a new '/goal' command. This command allows users to specify completion conditions, enabling the tool to operate autonomously. The update aims to enhance the self-driving capabilities of Claude Code for developers. AI

Google has seen a significant increase in internal Chrome vulnerability reports, with over 200 identified between March and May 2026. This surge appears to coincide with the company's integration of AI-powered security tools into its development process. The adoption of these AI tools may be contributing to the higher detection rate of security flaws within the Chrome browser. AI

The AI-powered code editor Antigravity, developed by Google, has transitioned from a VSCode-based platform to a standalone application. This evolution allows for enhanced capabilities and a more specialized user experience for developers. The author plans to utilize the updated editor to create a game reminiscent of Daytona USA. AI

36Kr and PureblueAI have launched a strategic partnership focused on the growing importance of AI recommendations for consumer brands. The collaboration aims to provide brands with insights into their visibility and ranking within AI search results and recommendation systems. Together, they released the "2026 Consumer Brand AI Recommendation Power List," with plans for future industry-specific publications to guide brands in the evolving AI landscape. AI

Other World Computing (OWC) has launched the OWC Stack AI, a new storage hub and AI accelerator. This device is notable for being the first to support Thunderbolt 5 technology. It aims to bring AI capabilities directly to users' workstations. AI

Researchers have developed Velocityformer, a novel equivariant graph transformer architecture designed to enhance the reconstruction of galaxy velocities for cosmological studies. This model specifically addresses the broken symmetry inherent in observational data, leading to a significant 35% improvement in the correlation coefficient compared to standard linear theory baselines. Velocityformer demonstrates high data efficiency, achieving accuracy with minimal simulations, and shows strong generalization capabilities across different input geometries and cosmological parameters. AI

Researchers have introduced DeepWeb-Bench, a new benchmark designed to evaluate the deep research capabilities of advanced language models. This benchmark presents more challenging tasks than existing ones, requiring extensive evidence gathering from multiple sources, reconciliation of conflicting information, and multi-step reasoning over extended periods. Initial evaluations on nine frontier models revealed that derivation and calibration failures, rather than retrieval issues, are the primary obstacles, with models exhibiting distinct error patterns and domain specialization. AI

Researchers have developed a new machine learning framework to improve the accuracy of Global Navigation Satellite Systems (GNSS) positioning, particularly in challenging urban environments. The system uses activation functions to transform machine learning predictions about signal quality into weights for a weighted least squares algorithm. Experiments in Hong Kong and Tokyo showed that sigmoid activation functions consistently provided the most significant improvements in positioning accuracy across various machine learning models and GNSS configurations. AI

Researchers have developed HITL-D, a new shared control framework that combines human input with diffusion-based AI policies for robotic manipulation tasks. This system assists users by providing autonomous updates to the end effector's orientation, reducing the need for complex joystick controls and lowering mental workload. User studies showed that HITL-D significantly improved task completion times and user satisfaction compared to traditional teleoperation. AI

Researchers have developed a new policy called Fisher-SEP to help planners decide when to supplement simulators with real-world experiments. The policy decomposes the simulator's value error into identifiable calibration shifts and unresolvable parametric residuals. It also distinguishes between local and reachability components of the value gap between simulator-optimal and true optimal policies. Two case studies demonstrate Fisher-SEP's effectiveness in optimizing experimental strategies for supply chains and public health interventions. AI

Researchers have introduced Equilibrium Reasoners (EqR), a novel framework that enables scalable reasoning in iterative neural network models. EqR hypothesizes that generalizable reasoning emerges from learning task-conditioned attractors, which are dynamical systems that stabilize on valid solutions. This approach allows models to adaptively allocate computational resources based on task difficulty, significantly improving accuracy on complex problems like Sudoku-Extreme by scaling test-time compute. AI

Researchers have introduced Uni-Edit, a novel approach to tuning Unified Multimodal Models (UMMs) that enhances image understanding, generation, and editing simultaneously. Unlike traditional methods that use complex multi-task training, Uni-Edit employs a single editing task, a single training stage, and a single dataset. This is achieved by developing an automated data synthesis pipeline that transforms visual question-answering data into sophisticated editing instructions, creating the Uni-Edit-148k dataset. Experiments show that tuning solely on Uni-Edit leads to comprehensive improvements across all three capabilities without additional operations. AI

Researchers have developed agent just-in-time (JIT) compilation to optimize web agent planning and scheduling, significantly reducing latency and improving accuracy. This new approach compiles natural language task descriptions into executable code, allowing for LLM calls, tool usage, and parallelization. The system includes a JIT-Planner for generating and validating code plans, and a JIT-Scheduler for exploring parallelization strategies using Monte Carlo estimation. Tests across five web applications showed a 10.4x speedup and 28% accuracy increase over existing methods, with the scheduler providing an additional 2.4x speedup and 9% accuracy improvement. AI

Researchers have developed a new method called interpretable rubric embeddings to address label bias in AI models trained on historical human evaluations. This approach replaces standard black-box embeddings with features derived from expert-defined criteria, aiming to prevent models from inheriting biases present in past decisions. Empirical evaluations on a dataset of master's program applications demonstrated that this method reduces group disparities while enhancing cohort quality, offering a practical solution for learning with biased labels. AI

Researchers have developed a new method using Large Language Models (LLMs) to automatically adapt grammars following metamodel evolution in model-driven engineering. This LLM-based approach learns adaptations from previous versions, outperforming traditional rule-based methods in consistency and output similarity on smaller datasets. While effective for complex grammar scenarios, the study found LLMs struggled with adaptation consistency on very large grammars, indicating limitations for large-scale applications. AI

Researchers have developed ProtoPathway, a novel multimodal framework designed for predicting cancer survival. This framework integrates whole slide imaging and transcriptomics data by using biologically grounded representations. ProtoPathway employs learnable morphological prototypes for image analysis and a graph neural network for genomic data, enabling cross-modal attention to model the relationship between molecular programs and tissue morphology. The system offers enhanced biological interpretability and reduced computational cost, demonstrating competitive performance on TCGA cancer cohorts. AI

A new survey paper delves into the mathematical underpinnings of neural network expressivity, focusing on approximation theory. It reviews classical density results for single-hidden-layer networks and explores quantitative bounds that link approximation error to network size and function smoothness. The paper also highlights depth-width trade-offs and introduces recent theoretical attention on Kolmogorov-Arnold Networks (KANs) as an alternative architectural paradigm. AI

Researchers have developed a method to test the robustness of driving-focused Vision-Language-Action (VLA) models by applying sensor perturbations. Their study on the Alpamayo R1 model revealed that changes in Chain-of-Causation (CoC) explanations directly correlate with significant deviations in driving trajectories. The findings suggest that reasoning consistency can serve as a reliable indicator for planning safety in autonomous driving systems. AI