A New Trick Reveals AI Models' Inner Thoughts Researchers devised a way to extract "reasoning traces" from Claude, GPT, and Gemini. What they found, they say, indicates that some Chinese AI may be trained on leading US models. Computer scientists recently discovered a way to extract the hidden "thinking" that frontier AI models perform as they work through complex problems. The findings provide some evidence--although not conclusive proof--that certain Chinese models may have been trained by " distilling " reasoning information from US models that was supposedly hidden because of how closely some of their thinking or reasoning patterns seem to match. The researchers have also demonstrated that the method could be used to recover personal information, like passwords and API keys, from a model's inner reasoning, although this vulnerability has been fixed.

China's Open AI Models Are Challenging Silicon Valley's Playbook As access to Anthropic's and OpenAI's frontier models becomes more restricted, Chinese labs are pitching their open-source alternatives as stable, accessible, and increasingly capable. The AI industry is not quite experiencing a DeepSeek 2.0 moment, but it feels very close. The leading Chinese AI labs have been on a roll lately, releasing a series of almost cutting-edge open-source models. Z.ai released GLM 5.2 in June, Moonshot AI released Kimi K3 last week, and Alibaba released Qwen 3.8 this Monday. Silicon Valley and Washington started talking about the models immediately, especially K3, which is widely seen as the best of the bunch.

In a low-rise building overlooking a busy intersection in Beijing, Ji Rong Wen, a middle-aged scientist with thin-rimmed glasses and a mop of black hair, excitedly describes a project that could advance one of the hottest areas of artificial intelligence. Wen leads a team at the Beijing Academy of Artificial Intelligence (BAAI), a government-sponsored research lab that's testing a powerful new language algorithm--something similar to GPT-3, a program revealed in June by researchers at OpenAI that digests large amounts of text and can generate remarkably coherent, free-flowing language. "This is a big project," Wen says with a big grin. "It takes a lot of computing infrastructure and money." Wen, a professor at Renmin University in Beijing recruited to work part-time at BAAI, hopes to create an algorithm that is even cleverer than GPT-3. He plans to combine machine learning with databases of facts, and to feed the algorithm images and video as well as text, in hope of creating a richer understanding of the physical world--that the words cat and fur don't just often appear in the same sentence, but are associated with one another visually.

The ticket kiosks at Shanghai's frenetic subway station have a mind of their own. Walk up to one and state your destination, and it'll automatically recommend a route before issuing a ticket. In the interest of reducing the rush-hour stampede, the system is set up to let you find information and buy tickets without pushing a button or talking to a person. More impressive still, all this happens successfully in the middle of a crowded, noisy station. Each kiosk has to figure out who is speaking to it; zero in on that person's voice within the crowd; transcribe the incoming speech; parse its meaning; and compare the person's face against a massive database of photos--all within a few seconds.