Search AI Products and News

Recently, the ModelScope community announced the release of a new dynamic benchmark dataset named UGMathBench. Its goal is to comprehensively assess the mathematical reasoning abilities of language models across a broad range of undergraduate mathematics subjects. The advent of this dataset fills the current gap in evaluating the reasoning capabilities of language models in the field of undergraduate mathematics and provides researchers with a richer and more challenging testing platform.

The Kimi team recently released a technical report and open-sourced the preview version of Kimina-Prover, including 1.5B and 7B parameter distilled models, the Kimina-Autoformalizer-7B model for data generation, and a revised miniF2F benchmark dataset. Kimina-Prover, jointly developed by the Numina and Kimi teams, is a mathematical theorem proving model that excels in the field of formal theorem proving.

Kunlun Wanwei's Tiangong team announced the launch of the upgraded Skywork-OR1 (Open Reasoner 1) series of models. This follows the February 2025 release of Skywork-o1, the company's first Chinese large language model focused on logical reasoning. The Skywork-OR1 series achieves industry-leading reasoning performance with the same parameter scale, further breaking through the bottlenecks in large model capabilities for logical understanding and complex task solving.

Alibaba is making waves in the AI field with the recent open-source release of its latest multimodal model, Qwen2.5-VL-32B-Instruct. This model is part of the Qwen2.5 series, which also includes 3B, 7B, and 72B versions. The 32B version prioritizes convenient local execution while maintaining performance. Enhanced through reinforcement learning, Qwen2.5-VL-32B excels in several areas. Notably, its responses are more aligned with human expectations.

Recently, the 360 ZhiNao team announced the successful replication of DeepSeek's reinforcement learning results and the official release of the open-source reasoning model Light-R1-14B-DS. This model surpasses DeepSeek-R1-Distill-Llama-70B and DeepSeek-R1-Distill-Qwen-32B in performance, becoming the industry's first 14B parameter model to achieve reinforcement learning effects. It significantly improves mathematical reasoning capabilities, outperforming most 32B-parameter models.

Today, the Alibaba Cloud Tongyi team officially released the new mathematical reasoning process reward model Qwen2.5-Math-PRM. This model offers two sizes, 72B and 7B, with performance significantly outperforming similar open-source process reward models, especially excelling in identifying reasoning errors. The 7B version of Qwen2.5-Math-PRM astonishingly surpasses the widely popular GPT-4o, marking an important step in Alibaba Cloud's research and development of reasoning models.

The Alibaba Qwen team recently published a paper titled 'Lessons Learned from the Development of Process Reward Models in Mathematical Reasoning' and introduced two new models in the Qwen2.5-Math-PRM series, featuring 7B and 72B parameters respectively. These models break through the limitations of the existing PRM framework in mathematical reasoning, significantly improving the accuracy and generalization ability of reasoning models through innovative techniques. Mathematical reasoning has long been a major challenge for large language models (LLMs), especially regarding errors in intermediate reasoning steps.

On the occasion of the annual completion at Beijing Zhihua Huazhang Technology Co., Ltd., the company released the first generation version of its inference model based on extended reinforcement learning technology — GLM-Zero-Preview. This model focuses on enhancing AI's reasoning capabilities, particularly excelling in mathematical logic, code writing, and handling complex problems that require deep reasoning. Compared to the base model, GLM-Zero-Preview shows significant improvements in specialized task capabilities while maintaining general task abilities.

Microsoft announced that its Phi series of generative AI models has a new member - Phi-4. Compared to previous versions, Phi-4 shows improvements in several areas, particularly in solving mathematical problems, thanks to the enhanced quality of training data. As of Thursday evening, access to Phi-4 is relatively limited, available only on Microsoft's newly launched Azure AI Foundry development platform and restricted to research purposes under the Microsoft Research Licensing Agreement. Image source note: Image generated by AI, image licensed.

In the vast universe of artificial intelligence, mathematics has long been considered the last bastion of machine intelligence. Now, a new benchmarking test named FrontierMath has emerged, pushing AI's mathematical reasoning capabilities to unprecedented limits. Epoch AI has collaborated with over 60 top minds in mathematics to create this challenge, which can be described as the 'Olympics of Mathematics' for AI. This is not just a technology test; it is the ultimate interrogation of artificial intelligence's mathematical wisdom. Imagine a laboratory filled with the world's top mathematicians, meticulously designing...

The Dark Side of the Moon Kimi Smart Assistant has announced the launch of its next-generation mathematical reasoning model, k0-math. The k0-math model has shown outstanding performance in multiple mathematical benchmark tests, surpassing OpenAI's o1 series models, o1-mini and o1-preview, in four mathematical benchmark assessments including middle school entrance exams, college entrance exams, graduate school entrance exams, and MATH competitions that include introductory problems.

In a recent sensational news in the mathematical community, a group of alumni from Tsinghua University has successfully proven 162 previously unsolved mathematical theorems with the help of AI. Even more impressively, this intelligent agent named LeanAgent has tackled Terence Tao's formal challenge regarding the Polynomial Freiman-Ruzsa Conjecture! This has led us to marvel at how AI has completely transformed the research methods in basic scientific studies. It is well known that current language models (LLMs), while impressive, still have limitations.

In the world of artificial intelligence, the reasoning capabilities of machine learning models, particularly large language models (LLMs), have been a focal point for scientists. Recently, Apple's AI research team published a paper titled 'Understanding the Limitations of Large Language Models in Mathematical Reasoning,' shedding light on these models' limitations when addressing logical problems. In the paper, researchers demonstrate this through a simple mathematical question. They first posed a question about Oliver picking kiwis: as follows: Oliver picked 44 on Friday.

Claude, the AI large model developed by Anthropic, has introduced a LaTeX formula rendering feature that significantly enhances the clarity and professionalism of mathematical expressions. Key highlights include improved consistency in formatting, standardized display of mathematical equations and expressions, making them easier to understand. The incorporation of LaTeX greatly enhances the clarity of answers, especially suitable for scenarios requiring solutions to mathematical problems. This feature meets the high demands of academic and professional fields, allowing users to quickly generate complex mathematical expressions with simple command inputs.