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Recently, a research team from the University of Hong Kong, The Chinese University of Hong Kong, and SenseTime has released a groundbreaking framework - GoT-R1. This new multimodal large model significantly enhances the semantic and spatial reasoning capabilities of AI in visual generation tasks by introducing reinforcement learning (RL), successfully generating high-fidelity and semantically consistent images from complex text prompts. This advancement marks another leap in image generation technology. Currently, although existing multimodal large models have made significant progress in generating images based on text prompts

Recently, the tech brand Fansang introduced its new portable private cloud product - FX2510 Smart NAS at the 2025 São Paulo Consumer Electronics and Appliance Exhibition (Eletrolar Show Brazil 2025). This upgraded NAS not only offers powerful storage capabilities but also deeply integrates interfaces of more than ten mainstream AI models, including ChatGPT and Deepseek, fully demonstrating the wide application of smart technology in the storage field. FX

Amid the rapid development of the global artificial intelligence industry, the US AI startup Anthropic officially announced on June 24 that it will open its first Asia-Pacific office in Tokyo this fall. This news undoubtedly injects new vitality into the AI ecosystem in Japan and the entire Asia-Pacific region. Founded in 2020, Anthropic is committed to developing AI technologies centered around human needs, with the philosophy of building safe and controllable AI systems to better serve society through technology. The company is leading in this field.

At the recently concluded Cannes Lions International Festival of Creativity, YouTube CEO Neal Mohan announced that YouTube will introduce its latest Veo3AI video generation model to YouTube Shorts later this summer. Dubbed "a rogue's dream", its release will bring new creative possibilities for short video creators. Currently, short video creators are already able to use the Veo2 model to generate background videos and standalone clips. Although Neil

A deep dive into how Eureka AI addresses three major pain points in R&D - information overload, innovation bottlenecks, and evaluation difficulties - through core features such as a technical Q&A assistant and TRIZ theory innovation. It provides end-to-end AI support solutions from concept to implementation.

A comprehensive analysis of the core functions of Patsnap's Eureka AI Agent, including creative generation, feasibility validation, intellectual property protection, and material R&D acceleration capabilities, to understand how AI can increase R&D efficiency by 3-5 times.

This article provides a comprehensive review of the innovation acceleration features of Patsnap's Eureka AI Agent, covering core capabilities such as creative generation, patent search, and cross-domain analysis, revealing how AI is transforming enterprise R&D processes.

The Meta AI research team has made another breakthrough in the artificial intelligence field, officially releasing the new video understanding model V-JEPA2 (Video Joint Embedding Predictive Architecture2) on June 11, 2025. Led by Meta's chief AI scientist Yann LeCun, this model has opened up new possibilities in video understanding and physical world modeling with its innovative self-supervised learning technology and zero-shot robot control capabilities.

According to a report by Bloomberg, in January this year, the low-cost and high-performance AI model launched by Chinese AI company DeepSeek caused a shock in the global technology community and brought significant urgency to China's tech giant Alibaba. To quickly catch up with this technological breakthrough, engineers at Alibaba canceled their vacation during the most important traditional Chinese festival — the Spring Festival — choosing to stay overnight in the company and fully commit themselves to AI R&D. Chairman of Alibaba, Joseph Tsai, vividly told this story of racing against time at the VivaTech Science and Technology Conference held in Paris on Wednesday.

Global renowned open-source AI platform Hugging Face recently released the open-weight model contributor ranking. Chinese teams Qwen and DeepSeek successfully ranked in the top 15, showcasing China's technical strength and influence in the global open-source AI field. This ranking honors the teams that provide high-quality model weights to the open-source community, with their models widely applied in academic and industrial innovation. The Qwen team supported by Alibaba Cloud Intelligence Group has gained favor from the community due to the excellent performance of its Qwen3 series models in tasks such as instruction following and code generation.

Recently, Sam Altman, CEO of OpenAI, announced on social platform X that the open source model originally scheduled for release in early summer will now be delayed until late summer. This news came as a surprise to industry insiders who were all looking forward to the launch. However, Altman explained that this delay was due to some "unexpected and quite impressive" achievements made by the team during the R&D process. In his post, Altman mentioned that OpenAI wants to spend more time on the weights of the open source model to ensure the best possible outcome.

The field of artificial intelligence continues to develop rapidly, and DeepSeek's latest update for its R1-0528 model has drawn significant attention from the developer community. According to AIbase, the upgraded DeepSeek R1-0528 now supports local tool calls on OpenRouter, a major improvement that enables developers to seamlessly integrate local tools into their AI workflows. Local tool calls enhance workflow efficiency. DeepSeek R1-0528 is an open-source inference model that rivals Ope

A new study led by Apple researchers has poured cold water on the current large reasoning models (LRMs) that are highly anticipated. The research found that when solving complex tasks, reasoning models designed to simulate thinking processes, such as Claude3.7Thinking and Deepseek-R1, not only failed to show advantages but also exhibited serious problems like insufficient reasoning and performance collapse. This study tested four classic logical puzzles: the Tower of Hanoi, Checkers, River Crossing, and Block World. These puzzles allow precise control over the tasks.