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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

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.

Recently, DeepSeek released an updated version of its latest R1 reasoning AI model, which demonstrated excellent performance in multiple math and coding benchmarks. However, DeepSeek did not disclose the source of the training data for its model, leading some AI researchers to question it and speculate that the model may have been partially trained based on Google's Gemini AI series. Melbourne developer Sam Paeach claimed that he found...

Recently, the Natural Language Intelligence Team of the Tongyi Lab officially released and open-sourced VRAG-RL — a multi-modal RAG reasoning framework driven by visual perception. It aims to solve the problem of how AI can retrieve key information and conduct fine-grained reasoning from visual languages such as images, tables, design drafts, etc., in real-world business scenarios. Retrieving and reasoning about key information in complex visual document knowledge bases has always been a major challenge in the AI field. Traditional retrieval-augmented generation (RAG) methods struggle when handling visually rich information because they find it difficult to...

Recently, Meta's FAIR team and researchers from the Hebrew University of Jerusalem jointly released a new study indicating that reducing the reasoning time of large language models can significantly improve their performance in complex reasoning tasks. The research findings show that using shorter reasoning chains, the accuracy of AI models has increased by 34.5%, which challenges some assumptions in the current AI industry. Image source notes: The image was generated by AI and is licensed by Midjourney in this study, the authors point out that prolonged deliberation

DeepSeek recently made significant updates to its high-performance reasoning AI model, DeepSeek-R1, greatly enhancing the model's performance in code generation and complex reasoning tasks, drawing widespread attention in the artificial intelligence community. Below is a comprehensive analysis of the key highlights of this update based on public information and the latest developments. R1 Model Update: Significant Improvement in Code Ability The latest update to DeepSeek-R1 has achieved remarkable breakthroughs in code generation capabilities. Testing shows that the new version of R1 significantly improves its ability to handle complex coding tasks.

Recently, a new evaluation benchmark - RBench-V, specifically designed to test the visual reasoning capabilities of multi-modal large models, was released by research teams from Tsinghua University, Tencent HUNYUAN, Stanford University, and Carnegie Mellon University. The introduction of this benchmark aims to fill the gap in the current evaluation system regarding the model's visual output capabilities, allowing for a more comprehensive understanding of existing model performance. The RBench-V benchmark consists of 803 questions covering multiple fields, including geometry and graph theory, mechanics and electromagnetism, multi-target recognition, and path planning.