#artificial-intelligence News & Analysis

Anthropic’s refusal to arm AI is exactly why the UK wants it

JPMorgan CEO says AI will transform banking faster than the internet era

Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids

Researchers have developed SmartGuard Energy Intelligence System (SGEIS), an AI framework that combines machine learning, deep learning, and graph neural networks to detect electricity theft in smart grids. The system achieved 96% accuracy in identifying high-risk nodes and demonstrates strong performance with practical applications for energy security.

VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model

HighFM: Towards a Foundation Model for Learning Representations from High-Frequency Earth Observation Data

Researchers have developed HighFM, a foundation model for analyzing high-frequency Earth observation data using over 2TB of satellite imagery to enable real-time disaster monitoring. The model adapts masked autoencoding frameworks with temporal encodings to capture short-term environmental changes and demonstrates superior performance in cloud masking and fire detection tasks.

Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices

Researchers introduce a new framework for evaluating adaptive AI models in medical devices, using three key measurements: learning, potential, and retention. The approach addresses challenges in assessing AI systems that continuously update, providing insights for regulatory oversight of adaptive medical AI safety and effectiveness.

Anthropic reveals $30bn run rate and plans to use 3.5GW of new Google AI chips

Anthropic has revealed a $30 billion annual revenue run rate and announced plans to deploy 3.5 gigawatts of new Google AI chips for its operations. This represents a significant scaling milestone for the AI company and demonstrates substantial growth in the artificial intelligence sector.

Aligning Progress and Feasibility: A Neuro-Symbolic Dual Memory Framework for Long-Horizon LLM Agents

Researchers propose a new Neuro-Symbolic Dual Memory Framework that addresses key limitations in large language models for long-horizon decision-making tasks. The framework separates semantic progress guidance from logical feasibility verification, significantly improving performance on complex AI tasks while reducing errors and inefficiencies.

LLM Reasoning with Process Rewards for Outcome-Guided Steps

Researchers introduce PROGRS, a new framework that improves mathematical reasoning in large language models by using process reward models while maintaining focus on outcome correctness. The approach addresses issues with current reinforcement learning methods that can reward fluent but incorrect reasoning steps.

What Is The Political Content in LLMs' Pre- and Post-Training Data?

Research reveals that large language models exhibit political biases stemming from systematically left-leaning training data, with pre-training datasets containing more politically engaged content than post-training data. The study finds strong correlations between political stances in training data and model behavior, with biases persisting across all training stages.

R2-Write: Reflection and Revision for Open-Ended Writing with Deep Reasoning

Researchers introduce R2-Write, a new AI framework that improves large language models' performance on open-ended writing tasks by incorporating explicit reflection and revision patterns. The study reveals that existing reasoning models show limited gains in creative writing compared to mathematical tasks, prompting the development of an automated system with writer-judge interactions and process reward mechanisms.

High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination

Research comparing large language models (LLMs) to humans in group coordination tasks reveals that LLMs exhibit excessive volatility and switching behavior that impairs collective performance. Unlike humans who adapt and stabilize over time, LLMs fail to improve across repeated coordination games and don't benefit from richer feedback mechanisms.

Industrial policy for the Intelligence Age

The article outlines proposed industrial policy framework for the AI era, emphasizing people-first approaches to managing advanced intelligence development. The policy focuses on expanding economic opportunities, ensuring equitable distribution of AI-generated prosperity, and strengthening institutional resilience.

Musk Demands Wall Street Buy Grok AI Subscriptions to Work on SpaceX IPO

Microsoft to pour $10B into Japan for AI expansion, cyber defense, and talent development

Chatbots are now prescribing psychiatric drugs

Google unveils Gemma 4 as its most advanced open AI model for reasoning and agentic tasks

Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use

Bitcoin miner Soluna acquires $53M wind farm to power AI facility

KiloClaw targets shadow AI with autonomous agent governance

Vitalik Buterin warns of AI security risks, pushes for local-first systems

China’s Five-Year Plan details the targets for AI deployment

Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption

Beyond Symbolic Control: Societal Consequences of AI-Driven Workforce Displacement and the Imperative for Genuine Human Oversight Architectures

Trilitech broadens tokenized commodities push on Tezos with Metals.io