Search AI Products and News

Jieyue Xingchen and Yuanli Lingji have signed a strategic cooperation agreement in Beijing. Both parties will leverage their respective technological advantages to carry out in-depth cooperation in multimodal large model technology, intelligent terminal Agents, and embodied AI scenarios. The goal of this cooperation is to achieve "reasoning in the physical world", jointly developing an intelligent robot named "RoboAgent", and promoting the practical application of Artificial General Intelligence (AGI). At the signing ceremony, Dr. Jiang Daxin, founder and CEO of Jieyue Xingchen, and the co-founders of Yuanli Lingji...

Fudan University and Jieyue Xingchen, a leading domestic AI innovation company, recently announced the upcoming release of OmniSVG, an end-to-end multimodal SVG generation model. This news has quickly garnered widespread attention in the technology and design fields. According to AIbase, OmniSVG's core strength lies in its powerful generation capabilities, supporting vector graphic generation from simple icons to complex anime characters, providing a new intelligent solution for digital art creation. The launch of this model is poised to redefine the technical boundaries of vector graphic generation. Multimodal Generation: Flexible response.

Shanghai Jieyue Xingchen Intelligent Technology Co., Ltd. and Zhiyuan Robotics have officially signed a deep strategic cooperation agreement. Both parties will conduct in-depth cooperation in the fields of base large models and robot research and development, jointly exploring technological breakthroughs and application innovations of "large model + embodied robot." This cooperation involves world model technology research and development, data cooperation in the field of embodied intelligence, and the implementation of application scenarios such as new retail, aiming to promote the large-scale application of embodied intelligence technology in areas such as home services, new retail, and intelligent manufacturing.

In the fiercely competitive AI landscape, a million-dollar experiment is quietly revolutionizing large language model (LLM) training. Jieyue Xingchen's research team recently released groundbreaking findings. By utilizing nearly 1 million NVIDIA H800 GPU hours, they trained 3,700 models of varying sizes from scratch, accumulating a staggering 100 trillion tokens. This revealed a universal scaling law dubbed 'Step Law,' offering a novel guide for efficient LLM training.

In the 'Top Ten National Treasures' annual selection initiated by the State-owned Assets Supervision and Administration Commission of the State Council, China Telecom's self-developed Xingchen Model made the list thanks to its groundbreaking technological achievements. As the first full-size, full-modal, and domestically produced foundational model system in the country, the Xingchen Model demonstrates exceptional capabilities in semantics, speech, vision, and multimodal fields. In the semantic domain, the Xingchen Model has achieved significant breakthroughs. Leveraging a fully domestic 10,000-card cluster and training framework, this model reaches over 93% of the computational efficiency of NVIDIA's equivalent power, with a training duration ratio that is more efficient.