Ovis2 is the latest version of the Ovis series models proposed by Alibaba's international team. Compared to the previous version 1.6, Ovis2 has significant improvements in data construction and training methods. It not only enhances the capacity density of small models but also greatly improves chain of thought (CoT) reasoning capabilities through instruction fine-tuning and preference learning. Additionally, Ovis2 introduces video and multi-image processing capabilities, and enhances multilingual abilities and OCR capabilities in complex scenarios, significantly increasing the model's practicality.

Recently, the successful launch of the VLM-R1 project has brought new hope to this field. This project represents the successful migration of the R1 Method from the DeepSeek team into visual language models, indicating that AI's understanding of visual content will enter a whole new phase. The inspiration for VLM-R1 comes from last year's open-source R1 Method by DeepSeek, which leverages GRPO (Generative Reward Processing Optimization) reinforcement.

Recently, Google announced the release of a brand new Vision-Language Model (VLM) called PaliGemma 2 Mix. This model combines image processing and natural language processing capabilities, allowing it to understand visual information and text input simultaneously, generating corresponding outputs as needed. This marks a significant breakthrough in artificial intelligence technology for multi-task processing. PaliGemma 2 Mix boasts powerful features, integrating image description and optical character recognition.

Recently, Vectara released a report titled 'Hallucination Ranking', comparing the performance of different large language models (LLMs) in generating hallucinations while summarizing short documents. This ranking utilized Vectara's Hughes Hallucination Evaluation Model (HHEM-2.1), which is regularly updated to assess the frequency of false information introduced by these models in summaries. According to the latest data, the report highlighted key metrics such as hallucination rates, factual consistency rates, response rates, and average summary lengths of several popular models.

Artificial intelligence is sweeping across the globe at an unprecedented speed, but a significant issue is becoming increasingly prominent: while general-purpose AI is proficient in many skills, it often struggles with specific regional cultures and languages. This is particularly true in culturally and linguistically diverse areas such as the Middle East and South Asia, where users urgently require AI models that understand the 'local language'. Although large general models can handle multiple languages, they often reveal a lack of cultural depth and in-depth regional knowledge when dealing with scenarios rich in regional context.

Recently, Microsoft launched OmniParser V2.0, a new parsing tool designed to convert user interface (UI) screenshots into structured formats. OmniParser enhances the performance of UI agents based on large language models (LLM), helping users better understand and interact with the information on their screens. The tool's training dataset includes an interactive icon detection dataset, meticulously curated and automatically annotated from popular websites to highlight clickable and actionable areas.

A recent study released by the University of Munich, the Munich Machine Learning Center, and Adobe Research shows that 12 top AI language models, including GPT-4o, Gemini1.5Pro, and Llama-3.3-70B, face significant performance degradation in long-text conceptual reasoning tasks. Despite these models supporting context processing of at least 128,000 tokens, their ability to establish deep logical connections still has fundamental limitations. The research team developed NOLIMA (No Text Matching).

Hugging Face has recently launched an online learning course called 'Agent Course', aimed at helping learners deepen their understanding of the fundamentals and applications of intelligent agents. The course is rich in content and is divided into five modules, progressing from basic concepts of agents to final assessment assignments, assisting students in mastering the necessary skills. The first module is 'Welcome to the Course', providing an overview, guidelines, and required tools to ensure that learners have a solid foundation during their studies. Next is

Recently, Tencent Technology (Shenzhen) Co., Ltd. published a patent regarding a training method and related equipment for large language models on the Tianyancha app. The patent is titled 'Training Method, Device, Computer Equipment, and Storage Medium for Large Language Models' and aims to enhance the learning capacity and accuracy of large language models through innovative training methods. In the training process of large language models, traditional methods often rely on a single text summary, which may lead to model overfitting and negatively impact the accuracy and diversity of generated content. However, Tencent's new...

With the continuous development of artificial intelligence technology, the integration of visual and textual data has become a complex challenge. Traditional models often struggle to accurately parse structured visual documents such as tables, charts, infographics, and diagrams. This limitation impacts automated content extraction and comprehension capabilities, subsequently affecting applications in data analysis, information retrieval, and decision-making. In response to this demand, IBM recently released Granite-Vision-3.1-2B, a compact visual language model specifically designed for document understanding.

Realbotix, a leading manufacturer of humanoid robots and companion AI, is further expanding its technological capabilities by launching large language model (LLM) integration and advanced customization features. These new functionalities are set to debut in February 2025, allowing users to seamlessly connect with a variety of mainstream AI platforms including OpenAI's ChatGPT, Meta's Llama, Google's Gemini, and the newly launched DeepSeek R1.

In the current technological environment, artificial intelligence (AI) has sparked widespread discussion. Eerke Boiten, a professor of cybersecurity at De Montfort University, states that existing AI systems have fundamental flaws in management and reliability, and therefore should not be used in critical applications. Professor Boiten points out that most current AI systems rely heavily on large neural networks, particularly generative AI and large language models (like ChatGPT). The workings of these systems are relatively complex, even though each neural...

In the field of academic research, literature retrieval is a complex and crucial information acquisition task. Researchers need the capability to handle intricate, domain-specific search queries to meet detailed research requirements. However, existing academic search platforms, like Google Scholar, often struggle to address these complex research queries. For example, queries related to non-stationary reinforcement learning using the UCB method require stronger computational and analytical capabilities. Moreover, researchers typically need to spend a significant amount of time and effort manually browsing extensive academic literature when conducting literature reviews.

In today's digital world, the use of short text has become central to online communication. However, these texts often lack common vocabulary or context, posing numerous challenges for Artificial Intelligence (AI) during analysis. In response, Justin Miller, an English Literature graduate student and data scientist from the University of Sydney, proposed a novel approach that utilizes Large Language Models (LLMs) to gain deeper understanding and analysis of short texts. Miller's research focuses on how to analyze a vast array of short texts, such as social media profiles,

DeepSeek has officially released and open-sourced its latest large language model R1, which has shown outstanding performance and is considered comparable to OpenAI's official version o1. This initiative not only marks another significant breakthrough in domestic AI technology but also provides new options for AI developers worldwide. DeepSeek R1 has extensively applied reinforcement learning techniques in the post-training phase, significantly enhancing the model's reasoning capabilities even with minimal labeled data, in areas such as mathematics, coding, and natural language reasoning.

Recently, Zhejiang University and Alibaba DAMO Academy jointly released a remarkable study aimed at creating high-quality multimodal textbooks from educational videos. This innovative research not only provides new ideas for training large-scale language models (VLMs) but may also change the way educational resources are utilized. With the rapid development of artificial intelligence technology, the pre-training corpus of VLMs mainly relies on visual-text pairs and visually intertwined data. However, much of this current data comes from the web, where the correlation between text and images is weak, and the knowledge density is relatively low.

Recently, a study led by the Austrian Institute of Complexity Science (CSH) revealed that despite large language models (LLMs) excelling in various tasks, they show significant shortcomings when tackling advanced history questions. The research team tested three leading models, including OpenAI's GPT-4, Meta's Llama, and Google's Gemini, with disappointing results. Note: The image was generated by AI, and the image is licensed by Midjourney to evaluate these models in history.

In multimodal tasks, visual language models (VLMs) play a crucial role, such as in image retrieval, image captioning, and medical diagnosis. These models aim to align visual data with language data for more efficient information processing. However, current VLMs still face significant challenges in understanding negation. Negation is critical in many applications, such as distinguishing between "a room without windows" and "a room with windows." Despite significant progress made by VLMs, existing models fall short when it comes to handling negated statements.