Disrupting malicious uses of AI | February 2026
OpenAI's February 2026 threat report examines how malicious actors combine AI models with websites and social platforms. The report analyzes implications for detection and defense systems.
Why this matters: Organizations need to understand evolving AI-enabled threats to develop effective security measures and response strategies.
MARS: Margin-Aware Reward-Modeling with Self-Refinement
MARS is a new method that improves AI reward models by focusing data augmentation on the most ambiguous training examples. It provides theoretical and empirical improvements over uniform augmentation.
Why this matters: This makes AI alignment training more data-efficient and robust, reducing reliance on costly human feedback.
Advancing independent research on AI alignment
OpenAI is committing $7.5 million to The Alignment Project to fund independent AI alignment research. The funding supports work on AGI safety and security.
Why this matters: This investment could accelerate research into making advanced AI systems safer and more reliable.
Solving Parameter-Robust Avoid Problems with Unknown Feasibility using Reinforcement Learning
A new reinforcement learning method called Feasibility-Guided Exploration addresses parameter-robust avoidance problems with unknown feasibility. It simultaneously identifies feasible conditions and learns safe policies.
Why this matters: This approach could improve the safety and reliability of autonomous systems operating in uncertain environments.
Introducing Lockdown Mode and Elevated Risk labels in ChatGPT
OpenAI is introducing Lockdown Mode and Elevated Risk labels in ChatGPT. These features are designed to help organizations defend against prompt injection and AI-driven data exfiltration.
Why this matters: This provides new tools for enhancing security when using AI models in sensitive or organizational contexts.
Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment
Researchers propose a verification approach to improve vision-language-action alignment, achieving better results than scaling policy pre-training.
Why this matters: This study contributes to the development of more accurate and reliable general-purpose robots.
Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage
Researchers propose a new generative framework, Fun-DDPS, for forward and inverse modeling in Carbon Capture and Storage. It combines function-space diffusion models with neural operator surrogates to improve accuracy and efficiency.
Why this matters: This breakthrough could enhance the accuracy and efficiency of CCS modeling, a crucial step in mitigating climate change.
Biases in the Blind Spot: Detecting What LLMs Fail to Mention
Researchers developed a pipeline to detect biases in large language models that aren't explicitly stated in their reasoning.
Why this matters: This work provides a practical approach to automatically discovering biases in AI models, which can lead to more accurate and fair decision-making.
Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy
Researchers propose a Federated Learning solution that combines data privacy and explainability using Decision Trees and Differential Privacy.
Why this matters: This study contributes to the development of more transparent and secure machine learning models.
Bringing ChatGPT to GenAI.mil
OpenAI has deployed a custom ChatGPT on GenAI.mil for secure AI use by U.S. defense teams.
Why this matters: This development brings secure AI capabilities to U.S. defense teams, enhancing their operations.
Making AI work for everyone, everywhere: our approach to localization
OpenAI shares its approach to AI localization, adapting frontier models to local languages, laws, and cultures without compromising safety.
Why this matters: This approach aims to make AI more accessible and usable for people worldwide.