Large language models have recently evolved from fluent text generation to advanced reasoning across diverse domains, giving rise to reasoning language models. Among these domains, mathematical reasoning serves as a representative benchmark as it requires precise multi-step logic and abstract reasoning, which can be generalized to other tasks. While closed-source RLMs such as GPT-o3 demonstrate impressive reasoning capabilities, their proprietary nature limits transparency and reproducibility. Although many open-source projects aim to close this gap, most of them lack sufficient openness by omitting critical resources such as datasets and detailed training configurations, which hinders reproducibility. To contribute toward greater transparency in RLM development, we introduce the MiroMind-M1 series, a set of fully open-source RLMs built on the Qwen-2.5 backbone that match or exceed the performance of existing open-source RLMs. Specifically, our models are trained in two stages: SFT on a carefully curated corpus of 719K math-reasoning problems with verified CoT trajectories, followed by RLVR on 62K challenging and verifiable problems. To enhance the robustness and efficiency of the RLVR process, we introduce Context-Aware Multi-Stage Policy Optimization, an algorithm that integrates length-progressive training with an adaptive repetition penalty to encourage context-aware RL training. Our model achieves state-of-the-art or competitive performance and superior token efficiency among Qwen-2.5-based open-source 7B and 32B models on the AIME24, AIME25, and MATH benchmarks. To facilitate reproducibility, we release the complete stack: models (MiroMind-M1-SFT-7B, MiroMind-M1-RL-7B, MiroMind-M1-RL-32B); datasets (MiroMind-M1-SFT-719K, MiroMind-M1-RL-62K); and all training and evaluation configurations. We hope these resources will support further research and foster community advancement.

This study aims to share a teaching experience teaching sound design for audiovisual productions and compares different projects tackled by students. It is not intended to be a comparative analysis of different types of teaching but rather an analysis of different problems observed in different profiles of students of the subject who study it in different grades. The world of audio can be very interesting for a large part of the students, both those with creative and technical inclinations. Musical creation and production, synchronization with images, dubbing, etc. They are disciplines that are generally interesting but can have a very high barrier to entry due to their great technical complexity. Sometimes it can take weeks or even months for the uninitiated to begin to use audio editing programs with the necessary ease, which are not always particularly intuitive for students. Learning through the use of PBL methodologies generates, in our experience, results much superior to those that can be observed through the use of other teaching methods such as master classes. Students acquire technical skills while developing creative projects in which they get personally involved. Despite everything mentioned above, most interactions between teachers and students focus on aspects of technical correction. From different parameters in reverbs (such as pre-delay, decay, modulation...) to how to correctly adjust compressors, noise gates, etc.; The number of tools with which to work with audio is incredibly extensive, as well as many of its features that can present serious differences depending on their manufacturers.

The use of an efficient coverage planning method is key for autonomous navigation in agricultural environments, where a robot must cover large areas of crops. This paper generally reviews the current state of the art of coverage path planning methods. Two widely used techniques applicable to agricultural environments are described in detail. The first consists of breaking down a complex field with obstacles into simpler subregions known as cells, to subsequently generate a coverage pattern in each of them. The second analyzes spaces composed of parallel strips through which the robot must circulate, in order to find the optimal order of visiting strips that minimizes the total distance traveled. Additionally, the combination of both techniques is discussed in order to obtain a more efficient global coverage plan. This analysis was conceived to be implemented with the soybean crop weeding robot developed at CIFASIS (CONICET-UNR).

The terms "artificial intelligence," "machine learning" and "robotic process automation" (RPA) get thrown around synonymously, but differentiating between them is important to understanding how best to use them. Brian Campo, deputy chief technology officer at the Department of Homeland Security, clarified that RPA is essentially "automation" -- the act of putting manual tasks into a context or system where the same action can be done automatically and intrinsically. As for AI and machine learning, the difference comes down to how the data is used. "So machine learning is trying to take data and make it intrinsically more informative, trying to take those automated insights and figure them out and find them in new and interesting ways, uncovering things that we wouldn't necessarily be thinking about or something that wouldn't occur to the operator," he said on Federal Monthly Insights -- Cloud and Artificial Intelligence. "Now, artificial intelligence is sort of different than that, in that it's not about driving insights -- it's about actually making impacts to some operational activity."

Instead of hiring an architect, artificial intelligence software may one day be able to design your new home or office. AI is already influencing architecture. New technologies ranging from smart speakers to smart thermostats are changing the way architects think about living and workspace. But the architecture of the future that's designed by AI may be unique, according to the authors of a new paper in the International Journal of Architectural Computing. "The result is something new, different, alien, strange, and wonderfully beautiful--maybe the first genuine 21st-century architecture," Matias del Campo, an associate professor of architecture at the University of Michigan, and one of the researchers who carried out the study, said in an email interview.

This plan was designed as an experiment in combining modern and baroque style into a new image. As you can see the result neither resembles Baroque ot Modern explicitly, but rather results in a new plan condition. The estrangement of the plan, based on ideas of defamiliarization and speculative realism. Over the past few decades, artificial intelligence (AI) tools have been used to analyze data or complete basic tasks in an increasing number of fields, ranging from computer science to manufacturing, medicine, physics, biology and even artistic disciplines. Researchers at University of Michigan have recently been investigating the use of artificial intelligence (AI) in architecture.

Over the past few decades, artificial intelligence (AI) tools have been used to analyze data or complete basic tasks in an increasing number of fields, ranging from computer science to manufacturing, medicine, physics, biology and even artistic disciplines. Researchers at University of Michigan have recently been investigating the use of artificial intelligence (AI) in architecture. Their most recent paper, published in the International Journal of Architectural Computing, specifically explores the potential of AI as a tool to create new architectural designs. "My partner, Sandra Manninger, and myself, have a long-standing obsession with the idea to cross pollinate the fields of architecture and AI," Matias del Campo, one of the researchers who carried out the study, told Tech Xplore. "We first got in touch with AI research in 1998, when we were introduced to the OFAI (The Austrian Institute of Artificial Intelligence) through a mutual friend, Dr. Arthur Flexer and we held the first course in Machine Learning for Architecture at the University of Applied Arts in Vienna, in 2006."

New concerns over how artificial intelligence videos could spread fake news and even prompt a war. Forget fake news for a moment. Artificial intelligence is now able to generate a convincing video of a celebrity or public figure. For illicit purposes, these videos are called deepfakes and show a celebrity superimposed into an adult movie. A programmer finds existing video and audio for a known figure, then the AI takes over and creates a brand new version.

Airlines are coming off a rough six months of brand perception. Forget about mishandled luggage, the bigger problem is mishandled passengers. Customer service – or lack of it – has given the airline industry a "black eye." Even the most customer-focused airlines are not immune to computer outages and winter storms that cause thousands of flights to be canceled. And because of the negative publicity the industry has received, it is under a microscope.

In 2014, the term "fake news" hadn't yet become part of the American lexicon and the 2016 U.S. presidential race was only beginning to make headlines. But in California, a man named Jestin Coler was hard at work creating one of the most divisive media trends in modern history. Dubbed the godfather of the fake news industry, Coler's efforts began with publishing fabricated stories -- including an article about Colorado food stamp recipients using welfare benefits to buy marijuana -- that garnered enough traffic to generate tens of thousands of dollars a month in ad revenue. The idea quickly caught on. Competing sites sprang up around the world as other publishers raced to create fake news masterpieces of outrageous, conspiratorial, and highly partisan news ahead of the election.