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Recently, the ByteDance Seed team collaborated with the University of Hong Kong and Fudan University to introduce an innovative reinforcement learning training method called POLARIS. This method successfully enhances the mathematical reasoning capabilities of small models to levels comparable to those of large models through a carefully designed Scaling RL strategy, offering a new approach for optimizing small models in the field of artificial intelligence. Experimental results show that the 4 billion parameter open-source model Qwen3-4B trained using POLARIS achieved remarkable performance on AIME25 and AIME24 mathematical tests.

The Ima web version is officially launched. Users can access the knowledge base through a browser, solving issues with software downloads and system compatibility. The new version supports features such as highlighted notes and pop-up Q&A, making it more convenient and efficient. The team will continue to introduce convenient features, and users can mark it as a favorite to stay updated. The web version makes accessing knowledge more flexible and efficient, suitable for various usage scenarios.

Kunlun releases Skywork-R1V3.0, an open-source multimodal model achieving expert-level reasoning. It excels in physics (52.8), logic (59.7), and math (77.1), outperforming Claude-3.7-Sonnet and GPT-4.5 with only 25K training samples.....

Alibaba opens WebSailor AI agent (32B/72B), outperforming closed-source models. Galaxy Securities highlights AI Agent economy growth, citing firms like Focus Tech and JinCai Tech. Open-source lowers entry barriers, accelerating industry progress.....

Recently, the Ali Speech AI team announced the open source of ThinkSound, the world's first audio generation model supporting chain-of-thought reasoning. By introducing the chain-of-thought technology, this model breaks through the limitations of traditional video-to-audio technology in capturing dynamic visuals, achieving high-fidelity and strong synchronized spatial audio generation. This breakthrough marks a leap forward in AI audio technology, moving from 'image配音' to structured understanding of visual content.

Zhipu AI officially open-sources its latest general vision model, GLM-4.1V-Thinking, based on the GLM-4V architecture, which introduces a chain-of-thought reasoning mechanism, significantly enhancing its capabilities for complex cognitive tasks. The model supports multimodal inputs such as images, videos, and documents, and excels in diverse scenarios including long video understanding, image question answering, subject problem-solving, text recognition, document interpretation, grounding, GUI Agent, and code generation, covering a wide range of industry application needs. GLM-4.1V-9B-Thinking

In the digital age, efficient note-taking tools have become an essential part of knowledge management. Recently, a cross-platform AI note-taking software called NoteGen has quickly gained popularity. It supports five major platforms: Windows, MacOS, Linux, iOS, and Android, and offers free multi-device data synchronization. With native Markdown formatting and strong integration with third-party large models, it redefines the note-taking experience. Full platform support and free synchronization seamlessly connect NoteGen, thanks to its powerful cross-platform compatibility.

Recently, Sam Altman, CEO of OpenAI, published a new blog post, predicting that AI technology has crossed a critical threshold and will witness major breakthroughs in the coming years. From AI systems that discover novel insights in 2026 to robots capable of executing tasks in the real world by 2027, up to the great abundance of intelligence and energy in the 2030s, Altman's vision has sparked global discussions. The AIbase editorial team has compiled the latest information to provide you with an in-depth analysis of this blueprint for the future of AI and its impact on the world.

The French AI lab Mistral has officially launched its first series of reasoning models - Magistral, marking another significant progress in the AI field. This series includes two versions: Magistral Small and Magistral Medium, aiming to enhance logical reasoning capabilities in fields such as mathematics and physics. These models solve problems step-by-step, comparable to models like OpenAI's o3 and Google's Gemini2.5Pro.

Recently, Apple Inc. released a controversial paper pointing out significant defects in the reasoning abilities of current large language models (LLMs). This view quickly sparked heated discussions on social media, especially among senior software engineers on GitHub, such as Sean Goedecke, who strongly opposed it. He believed that Apple's conclusions were too one-sided and could not fully reflect the capabilities of reasoning models. Apple's paper pointed out that the performance of LLMs was unreliable when solving benchmark tests such as mathematics and programming. The research team from Apple adopted

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.