Search AI Products and News

This morning, Microsoft officially announced its latest innovative small-parameter model, Mu. This model has only 330 million parameters, yet it can match the performance of Microsoft's previously released Phi-3.5-mini, while being just one-tenth the size of Phi-3.5-mini. More notably, Mu can achieve a response speed of over 100 tokens per second on offline NPU laptops, which is a rare breakthrough in the field of small-parameter models. A major highlight of the Mu model is its support for setting intelligent features in Windows.

On June 18th, the artificial intelligence research institute OpenAI officially open-sourced its customer service agent framework, marking an important step in the company's layout of enterprise-level AI solutions. This framework aims to promote the practical commercial application of intelligent agent systems from laboratory environments to real-world scenarios by providing transparent toolchains and clear implementation examples.

In the rapid development of technology, the application of artificial intelligence (AI) is becoming more and more widespread. Many people are beginning to look forward to the rise of the "agent" technology. An agent refers to an intelligent program that can work autonomously and accomplish specific goals. Recently, discussions about AI agents have been flourishing, but existing technologies still have a long way to go before achieving true intelligent agents. Imagine if we could have an AI agent capable of automatically writing academic papers. It could delve into learning how to write papers, analyze data, and conduct literature reviews after receiving datasets and research fields...

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.

At the 2025 Intelligent Economy Forum, Shen Dou, Executive Vice President of Baidu Group, announced that Baidu Smart Cloud has launched a new large model for the financial industry called Qianfan Smart Finance. This move marks Baidu's deep involvement in the financial sector, aiming to provide more precise and efficient artificial intelligence solutions for financial institutions. Shen Dou pointed out that 65% of Central SOEs have chosen to work with Baidu Smart Cloud, demonstrating the strong market demand for smart cloud technology. Building an industry-specific large model is a complex process that not only requires a strong technical foundation but also...

Ant International officially launched its AI-as-a-Service (AIaaS) platform - Alipay+ GenAI Cockpit. This new platform is designed to empower fintech companies and super apps in building AI agents and AI-native financial services. It combines automated workflows with intelligent task orchestration, covering key fintech processes such as payment orchestration, customer guidance, compliance checks, fraud detection, dispute resolution, and performance optimization. This marks an important step in the practical application and scalability of intelligent agents in the financial industry. Financial future...

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.

Mistral recently launched its new Agents API, a framework designed for developers to simplify the creation of AI agents that can execute various tasks such as running Python code, generating images, and performing Retrieval-Augmented Generation (RAG). The launch of this API aims to provide a unified environment for large language models (LLMs) to interact with multiple tools and data sources in a structured and persistent manner.