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Anthropic is testing 'Claude Neptune v3', a new AI model with strong math skills, possibly rivaling OpenAI/Google. It may be a Claude4.5 precursor or breakthrough version, aiming to lead in the competitive AI market.....

Large Language Models (LLMs) have achieved significant progress in complex reasoning tasks by combining task prompts with large-scale reinforcement learning (RL), as demonstrated by models like Deepseek-R1-Zero, which directly apply reinforcement learning to base models, showcasing strong reasoning capabilities. However, this success is difficult to replicate across different base model families, especially within the Llama series. This raises a core question: what factors lead to inconsistent performance of different base models during reinforcement learning? How does reinforcement learning perform in

On June 23, NetEase Youdao officially launched and open-sourced its latest 'Ziyue 3' series large model, named 'Confucius3-Math' in English. This reasoning model, focused on mathematical education, can efficiently run on ordinary consumer-grade GPUs, becoming the first low-cost and high-performance AI educational tool in China. In a series of mathematical reasoning tasks, 'Ziyue 3' has demonstrated outstanding performance that surpasses many large-scale general models. The 'Ziyue Math Model' achieves this through large-scale reinforcement learning and a series of innovative algorithms.

While most delivery automation systems are still stuck at the sidewalk, Rivr wants to bring robots directly to your doorstep. The robotics startup, headquartered in Zurich, is pilot testing a four-wheeled robot that can climb stairs with US logistics company Veho in Austin, Texas. This robot aims to solve the last tough part of end-to-end delivery: the final 100 yards. Designed to complete the most complex and physically demanding leg of the journey between Veho's van and the customer's door, this robot is being put through its paces by Rivr founder and CEO.

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.

On April 24th, Kunlun Wanwei announced the official open-sourcing of its multimodal reasoning model, Skywork-R1V2.0 (hereinafter referred to as R1V2.0). This upgraded version demonstrates significant improvements in both visual and text reasoning capabilities, particularly excelling in deep reasoning for challenging science problems in the College Entrance Examination and general task scenarios. It is considered the currently most balanced open-source multimodal model, equally adept at visual and text reasoning.

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.

On March 6, 2025, a new language model called **Light-R1-32B** was officially launched. Built upon the **Qwen2.5-32B-Instruct** model, this mathematical problem-solving tool, specially trained for exceptional mathematical abilities, low training costs, and reproducibility, is a significant highlight in the field of artificial intelligence. The development team xAI stated that Light-R1-32B not only surpasses similar models in performance but also provides significant benefits for academic research and practical applications.

Zhou Hongyi discusses the challenges posed by AI hallucinations and their implications for cybersecurity. He emphasizes the need for a balanced approach, neither downplaying nor overstating the risks involved.

Recently, an AI system developed by Google DeepMind—AlphaGeometry2—has successfully surpassed the average level of gold medalists at the International Mathematics Olympiad (IMO), demonstrating outstanding performance in solving geometry problems. AlphaGeometry2 is an upgraded version of the AlphaGeometry system released by DeepMind last year. According to the latest research from the research team, this system can solve 84% of the geometry problems from the IMO over the past 25 years.