-A Chinese Startup Enters the Global RSI Race.
BEIJING, Sept. 16, 2026 /PRNewswire/ — Apex Intelligence has completed its angel and angel-plus funding rounds, raising nearly US$50 Million in total. The angel round was co-led by IDG Capital,LinkX Capital and XtalPi, with participation from Decent Capital, SEE Fund, Monad Ventures, and Winsoul Capital. The angel-plus round was co-led by Zhongguancun Science City Fund, SCGC, and Shanghai Engine Fund.
Founded in June 2026, Apex Intelligence is building the next generation of self-evolving foundation models. Self-evolution refers to Recursive Self-Improvement (RSI), or models improving themselves. With a focus on scientific research and discovery, the company aims to move AI from an assistive tool to a co-researcher, and from accelerating what is known to discovering what is unknown.
Apex Intelligence will also visit leading North American universities—including Harvard University, MIT, Yale University, and Boston University—from September 17 to September 23 to meet with students, researchers, and academic research groups for talent recruitment and research collaboration.
AI’s Next Frontier Is Research
Centuries of technological progress have unfolded through successive S-curves opened up by scientific breakthroughs. Each S-curve spans three stages: scientific breakthroughs, research discoveries, and engineering implementation. Most AI today remains focused on engineering and execution, while the scarce breakthroughs in science and research still depend heavily on humans.
Apex Intelligence believes the next technological revolution will center on AI becoming a co-researcher and a native research engine—one that goes beyond accelerating familiar work to actively discovering unexplored directions. Research is the core driving force behind RSI.
Building AI Researchers, Not Just AI Assistants
Apex Intelligence believes that advancing AI’s general intelligence in R&D requires model training. Specifically, through mid-training and post-training, the company guides models to generate creative ideas and autonomously carry out the entire cycle of self-improvement: hypothesis, experimentation, validation, and iteration.
Over the long term, Apex Intelligence aims to build self-evolving foundation models whose collective research capabilities match, and progressively surpass, those of 100,000 top-tier AI scientists spanning disciplines, forming a scalable network of AI researchers. This is a long-term vision and goal, and the company is working toward it step by step. It represents a fundamental shift in the R&D paradigm, rather than an incremental improvement in efficiency. Potential applications span any field where R&D workflows can be structured and outcomes measured, including chip design and simulation, molecular R&D, and quantitative strategy iteration. These fields share three characteristics: high value, intensive demands on human expertise, and costly trial and error.
Apex Intelligence’s Self-Evolving AI System Approaches Capabilities of Leading Human Researchers
Apex Intelligence’s self-evolving AI system has delivered encouraging results in both AI for AI and AI for Math.
AI for AI
The system has autonomously produced research of a standard suitable for acceptance at leading AI conferences, demonstrating capabilities comparable to those of an AI PhD student. It has also autonomously achieved more accurate decisions before training, better strategies during training, and optimizations to the underlying GPU kernels. Across SimpleTES, NanoChat Autoresearch, GPUMode TriMul, and MLS-Bench, it has set new records or approached the best publicly reported results, outperforming solutions developed by or with contributions from Recursive Superintelligence, Tencent Hunyuan, Stanford, NVIDIA, and other organizations.
AI for Math
The AI system has produced a complete proof of the majorization conjecture, which had remained open for more than three decades, and achieved multiple breakthroughs in optimization theory, geometric topology, and other areas.
Apex Intelligence was founded by Yongchao Chen, an assistant professor at Tsinghua University’s School of Artificial Intelligence. His undergraduate and graduate education spans the University of Science and Technology of China and a joint Harvard–MIT training program. He has conducted research at Google DeepMind, Microsoft, and the MIT–IBM Watson AI Lab. The core team comes from leading universities including Tsinghua, Peking University, Harvard, and MIT.
Looking ahead, Apex Intelligence will continue investing in foundation models and compute, advance the training and iteration of recursively self-improving foundation models, build a high-quality research trajectory data infrastructure, and attract top talent from around the world. Guided by its founding mission to “Unlock Undiscovered Discovery,” the company is working to move AI from reproducing existing knowledge to autonomous scientific discovery.
Founder Quotes
“I have always believed that models taught by humans are ultimately limited by humans themselves. When we align them to human preferences, we lock their intelligence ceiling at our own level. No matter how much compute and data we add, we are only making them better at retaining existing knowledge. The limits of true intelligence can only be defined by the objective world. The first generation of large models replaces white-collar workers; embodied AI replaces blue-collar workers; and ASI will replace humanity’s most accomplished scientists. That moment will come sooner or later. Our job is to bring it closer.”
— Yongchao Chen, Founder of Apex Intelligence
About Apex Intelligence
Founded in June 2026, Apex Intelligence is a Beijing, China-based self-evolving foundation model company focused on next-generation industrial intelligence through recursive self-improvement.
For more information, please visit: https://apexin.ai/
SOURCE Apex Intelligence