Tencent releases versatile open-source Hunyuan AI modelsAI Newson August 4, 2025 at 2:58 pm Tencent has expanded its family of open-source Hunyuan AI models that are versatile enough for broad use. This new family of models is engineered to deliver powerful performance across computational environments, from small edge devices to demanding, high-concurrency production systems. The release includes a comprehensive set of pre-trained and instruction-tuned models available on the developer
The post Tencent releases versatile open-source Hunyuan AI models appeared first on AI News.
Tencent has expanded its family of open-source Hunyuan AI models that are versatile enough for broad use. This new family of models is engineered to deliver powerful performance across computational environments, from small edge devices to demanding, high-concurrency production systems. The release includes a comprehensive set of pre-trained and instruction-tuned models available on the developer
The post Tencent releases versatile open-source Hunyuan AI models appeared first on AI News. Read More
These protocols will help AI agents navigate our messy livesMIT Technology Reviewon August 4, 2025 at 3:00 pm A growing number of companies are launching AI agents that can do things on your behalf—actions like sending an email, making a document, or editing a database. Initial reviews for these agents have been mixed at best, though, because they struggle to interact with all the different components of our digital lives. Part of the…
A growing number of companies are launching AI agents that can do things on your behalf—actions like sending an email, making a document, or editing a database. Initial reviews for these agents have been mixed at best, though, because they struggle to interact with all the different components of our digital lives. Part of the… Read More
World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networkscs.AI updates on arXiv.orgon August 4, 2025 at 4:00 am arXiv:2505.01712v2 Announce Type: replace
Abstract: Traditional reinforcement learning (RL)-based learning approaches for wireless networks rely on expensive trial-and-error mechanisms and real-time feedback based on extensive environment interactions, which leads to low data efficiency and short-sighted policies. These limitations become particularly problematic in complex, dynamic networks with high uncertainty and long-term planning requirements. To address these limitations, in this paper, a novel world model-based learning framework is proposed to minimize packet-completeness-aware age of information (CAoI) in a vehicular network. Particularly, a challenging representative scenario is considered pertaining to a millimeter-wave (mmWave) vehicle-to-everything (V2X) communication network, which is characterized by high mobility, frequent signal blockages, and extremely short coherence time. Then, a world model framework is proposed to jointly learn a dynamic model of the mmWave V2X environment and use it to imagine trajectories for learning how to perform link scheduling. In particular, the long-term policy is learned in differentiable imagined trajectories instead of environment interactions. Moreover, owing to its imagination abilities, the world model can jointly predict time-varying wireless data and optimize link scheduling in real-world wireless and V2X networks. Thus, during intervals without actual observations, the world model remains capable of making efficient decisions. Extensive experiments are performed on a realistic simulator based on Sionna that integrates physics-based end-to-end channel modeling, ray-tracing, and scene geometries with material properties. Simulation results show that the proposed world model achieves a significant improvement in data efficiency, and achieves 26% improvement and 16% improvement in CAoI, respectively, compared to the model-based RL (MBRL) method and the model-free RL (MFRL) method.
arXiv:2505.01712v2 Announce Type: replace
Abstract: Traditional reinforcement learning (RL)-based learning approaches for wireless networks rely on expensive trial-and-error mechanisms and real-time feedback based on extensive environment interactions, which leads to low data efficiency and short-sighted policies. These limitations become particularly problematic in complex, dynamic networks with high uncertainty and long-term planning requirements. To address these limitations, in this paper, a novel world model-based learning framework is proposed to minimize packet-completeness-aware age of information (CAoI) in a vehicular network. Particularly, a challenging representative scenario is considered pertaining to a millimeter-wave (mmWave) vehicle-to-everything (V2X) communication network, which is characterized by high mobility, frequent signal blockages, and extremely short coherence time. Then, a world model framework is proposed to jointly learn a dynamic model of the mmWave V2X environment and use it to imagine trajectories for learning how to perform link scheduling. In particular, the long-term policy is learned in differentiable imagined trajectories instead of environment interactions. Moreover, owing to its imagination abilities, the world model can jointly predict time-varying wireless data and optimize link scheduling in real-world wireless and V2X networks. Thus, during intervals without actual observations, the world model remains capable of making efficient decisions. Extensive experiments are performed on a realistic simulator based on Sionna that integrates physics-based end-to-end channel modeling, ray-tracing, and scene geometries with material properties. Simulation results show that the proposed world model achieves a significant improvement in data efficiency, and achieves 26% improvement and 16% improvement in CAoI, respectively, compared to the model-based RL (MBRL) method and the model-free RL (MFRL) method. Read More
A ChatGPT-based approach for questions generation in higher educationcs.AI updates on arXiv.orgon July 31, 2025 at 4:00 am arXiv:2507.21174v2 Announce Type: replace-cross
Abstract: Large language models have been widely applied in many aspects of real life, bringing significant efficiency to businesses and offering distinctive user experiences. In this paper, we focus on exploring the application of ChatGPT, a chatbot based on a large language model, to support higher educator in generating quiz questions and assessing learners. Specifically, we explore interactive prompting patterns to design an optimal AI-powered question bank creation process. The generated questions are evaluated through a “Blind test” survey sent to various stakeholders including lecturers and learners. Initial results at the Banking Academy of Vietnam are relatively promising, suggesting a potential direction to streamline the time and effort involved in assessing learners at higher education institutes.
arXiv:2507.21174v2 Announce Type: replace-cross
Abstract: Large language models have been widely applied in many aspects of real life, bringing significant efficiency to businesses and offering distinctive user experiences. In this paper, we focus on exploring the application of ChatGPT, a chatbot based on a large language model, to support higher educator in generating quiz questions and assessing learners. Specifically, we explore interactive prompting patterns to design an optimal AI-powered question bank creation process. The generated questions are evaluated through a “Blind test” survey sent to various stakeholders including lecturers and learners. Initial results at the Banking Academy of Vietnam are relatively promising, suggesting a potential direction to streamline the time and effort involved in assessing learners at higher education institutes. Read More
KIX: A Knowledge and Interaction-Centric Metacognitive Framework for Task Generalizationcs.AI updates on arXiv.orgon July 31, 2025 at 4:00 am arXiv:2402.05346v3 Announce Type: replace
Abstract: People aptly exhibit general intelligence behaviors through flexible problem-solving and the ability to adapt to novel situations by reusing and applying high-level knowledge acquired over time. In contrast, artificial agents tend to be specialists, lacking such generalist behaviors. To bridge this gap, artificial agents will require understanding and exploiting critical structured knowledge representations. We introduce a metacognitive reasoning framework, Knowledge-Interaction-eXecution (KIX), and argue that interactions with objects, by leveraging a type space, facilitate the learning of transferable interaction concepts and promote generalization. This framework offers a principled approach for integrating knowledge into reinforcement learning and holds promise as an enabler for generalist behaviors in artificial intelligence, robotics, and autonomous systems.
arXiv:2402.05346v3 Announce Type: replace
Abstract: People aptly exhibit general intelligence behaviors through flexible problem-solving and the ability to adapt to novel situations by reusing and applying high-level knowledge acquired over time. In contrast, artificial agents tend to be specialists, lacking such generalist behaviors. To bridge this gap, artificial agents will require understanding and exploiting critical structured knowledge representations. We introduce a metacognitive reasoning framework, Knowledge-Interaction-eXecution (KIX), and argue that interactions with objects, by leveraging a type space, facilitate the learning of transferable interaction concepts and promote generalization. This framework offers a principled approach for integrating knowledge into reinforcement learning and holds promise as an enabler for generalist behaviors in artificial intelligence, robotics, and autonomous systems. Read More
The two people shaping the future of OpenAI’s researchMIT Technology Reviewon July 31, 2025 at 9:06 am For the past couple of years, OpenAI has felt like a one-man brand. With his showbiz style and fundraising glitz, CEO Sam Altman overshadows all other big names on the firm’s roster. Even his bungled ouster ended with him back on top—and more famous than ever. But look past the charismatic frontman and you get…
For the past couple of years, OpenAI has felt like a one-man brand. With his showbiz style and fundraising glitz, CEO Sam Altman overshadows all other big names on the firm’s roster. Even his bungled ouster ended with him back on top—and more famous than ever. But look past the charismatic frontman and you get… Read More
LLMs and Mental HealthTowards Data Scienceon July 31, 2025 at 3:01 pm Are LLMs good or bad for our mental health? It’s more complicated than that.
The post LLMs and Mental Health appeared first on Towards Data Science.
Are LLMs good or bad for our mental health? It’s more complicated than that.
The post LLMs and Mental Health appeared first on Towards Data Science. Read More
How to Benchmark LLMs – ARC AGI 3Towards Data Scienceon July 31, 2025 at 3:08 pm Learn how to LLMs are benchmarked, and try out the newly released ARC AGI 3
The post How to Benchmark LLMs – ARC AGI 3 appeared first on Towards Data Science.
Learn how to LLMs are benchmarked, and try out the newly released ARC AGI 3
The post How to Benchmark LLMs – ARC AGI 3 appeared first on Towards Data Science. Read More
The ONLY Data Science Roadmap You Need to Get a JobTowards Data Scienceon July 31, 2025 at 2:52 pm Are you looking to become a data scientist and don’t know where to start? In this article, I want to provide you with a straightforward, no-nonsense learning roadmap that you can follow to break into the industry. By the end, you’ll finally have a clear understanding of what is required and the best resources to
The post The ONLY Data Science Roadmap You Need to Get a Job appeared first on Towards Data Science.
Are you looking to become a data scientist and don’t know where to start? In this article, I want to provide you with a straightforward, no-nonsense learning roadmap that you can follow to break into the industry. By the end, you’ll finally have a clear understanding of what is required and the best resources to
The post The ONLY Data Science Roadmap You Need to Get a Job appeared first on Towards Data Science. Read More
Alibaba’s AI coding tool raises security concerns in the WestAI Newson July 30, 2025 at 10:00 am Alibaba has released a new AI coding model called Qwen3-Coder, built to handle complex software tasks using a large open-source model. The tool is part of Alibaba’s Qwen3 family and is being promoted as the company’s most advanced coding agent to date. The model uses a Mixture of Experts (MoE) approach, activating 35 billion parameters
The post Alibaba’s AI coding tool raises security concerns in the West appeared first on AI News.
Alibaba has released a new AI coding model called Qwen3-Coder, built to handle complex software tasks using a large open-source model. The tool is part of Alibaba’s Qwen3 family and is being promoted as the company’s most advanced coding agent to date. The model uses a Mixture of Experts (MoE) approach, activating 35 billion parameters
The post Alibaba’s AI coding tool raises security concerns in the West appeared first on AI News. Read More