How to create three easy Halloween makeup looks with GlowUp's Axel Axel A D Brown was one of the final three close MUA An abbreviation of makeup artist on series 5 of Glow Up. Their background is in drag, sci-fi and nerd culture. This means Axel's take on using make up to create glamorous creatures and creepy monsters is perfect for this time of year. There's something special about Halloween that allows people to let their guard down and fully express themselves, Axel says. Nobody really cares what you're doing because everyone's crazy and weird looking." BBC Bitesize asked Axel to show us how to create three seriously spooky glow-ups. How to paint a frightening Frankenstein's monster head Start with a clean, moisturised face. Green, white, red and black makeup is all you need to complete this look. Slide 1 of 4, Man with a clean face dressed in a black shirt, Step 1 Start with a clean, moisturised face. Go full-on with the orange face paint and get ready to do some more line work. Use a fine paint brush and if you're worried about wobbly lines, try holding your elbow while you draw as this can steady a shaky hand Don't worry if you don't have any - you can achieve the same look with a green water-based paint, just add clear hairspray to help it stay on all night Axel loves to do makeup looks based on creatures and monsters. "Whenever I'm sketching out ideas, the first thing that comes to me is usually a colour combination that intrigues me, Axel says.

Critical thinking is essential for building robust AI systems, preventing them from blindly accepting flawed data or biased reasoning. However, prior work has primarily focused on passive critical thinking, where models simply reject problematic queries without taking constructive steps to address user requests. In this work, we introduce proactive critical thinking, a paradigm where models actively seek missing or clarifying information from users to resolve their queries better. To evaluate this capability, we present GSM-MC and GSM-MCE, two novel benchmarks based on GSM8K for assessing mathematical reasoning under incomplete or misleading conditions. GSM-MC contains 1,368 math problems with a key variable deliberately removed, requiring models to identify and request the missing information. GSM-MCE further increases the difficulty by introducing irrelevant details to test robustness against distractions. Experiments on Qwen3 and Llama series models show that, while these models excel in traditional reasoning tasks due to extensive post-training and inference-time scaling, they struggle with proactive critical thinking, especially smaller ones. However, we demonstrate that reinforcement learning (RL) can significantly improve this ability. Using our enhanced RL algorithm, we achieve substantial gains, boosting the Qwen3-1.7B's accuracy from 0.15% to 73.98% on GSM-MC. We hope this work advances models that collaborate more effectively with users in problem-solving through proactive critical thinking.

One of the challenges of exploring distant worlds is the variety of terrains that a vehicle might encounter there. There could be flat planes, which are relatively easy to traverse in a wheeled vehicle, and there could be steep slopes, which are much harder. That's why NASA is developing a new type of rover that can transform to take a shape most suited to the environment. The DuAxel rover is made up of two individual rovers with two wheels each, both called Axel. Together, the four-wheeled rover can travel across rugged terrain and drive across considerable distances.