"The vast majority of our [enterprise and startup] customers are already using a lot of open models," said Casey Dvorak, a research program manager at OpenAI, in a media briefing about the model release. "Because there is no [competitive] open model from OpenAI, we wanted to plug that gap and actually allow them to use our technology across the board." The new models come in two different sizes, the smaller of which can theoretically run on 16 GB of RAM--the minimum amount that Apple currently offers on its computers. The larger model requires a high-end laptop or specialized hardware. Open models have a few key use cases.

ChatGPT will not tell people to break up with their partner and will encourage users to take breaks from long chatbot sessions, under new changes to the artificial intelligence tool. OpenAI, ChatGPT's developer, said the chatbot would stop giving definitive answers to personal challenges and would instead help people to mull over problems such as potential breakups. "When you ask something like: 'Should I break up with my boyfriend?' ChatGPT shouldn't give you an answer. It should help you think it through – asking questions, weighing pros and cons," said OpenAI.

OpenAI has introduced a new feature in ChatGPT that prompts users to take breaks during longer conversations. The reminders appear as pop-up messages like: "You've been chatting for a while -- is it time for a break?" Users can still dismiss the reminder and continue the conversation. OpenAI says that in the future, ChatGPT will also become better at handling sensitive topics and avoid giving direct answers to big personal decisions. Instead, the AI will help users reflect on different options.

Anthropic and Google are among the companies and groups working to fix that. Over the past year, they have both introduced protocols that try to define how AI agents should interact with each other and the world around them. If they work as planned, they could give us a crucial part of the infrastructure we need for agents to be useful. Read our story to learn more. OpenAI has given itself a dual mandate: on the one hand, it's a tech giant rooted in products, including of course ChatGPT, which people around the world reportedly send 2.5 billion messages to each day. But its original mission is as a research lab that will not only create "artificial general intelligence" but ensure that it benefits all of humanity.

As Will points out, there were two recent wins for OpenAI in its efforts to build AI that outcompetes humans. Its models took second place at a top-level coding competition and--alongside those from Google DeepMind--achieved gold-medal-level results in the 2025 International Math Olympiad. People who believe that AI doesn't pose genuine competition to human-level intelligence might actually take some comfort in that. AI is good at the mathematical and analytical, which are on full display in olympiads and coding competitions. That doesn't mean it's any good at grappling with the messiness of human emotions, making hard decisions, or creating art that resonates with anyone. But that distinction--between machine-like reasoning and the ability to think creatively--is not one OpenAI's heads of research are inclined to make.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. This time, we hear from Shaghayegh (Shirley) Shajarian and learn about her research applying generative AI to computer networks. I am a third-year PhD student in the Computer Science department at North Carolina A&T State University, working under Dr Sajad Khorsandroo and Dr Mahmoud Abdelsalam. I am part of the Autonomous Cybersecurity and Resilience Lab, where my research focuses on applying generative AI to computer networks. I am developing AI-driven agents that assist with some network operations, such as log analysis, troubleshooting, and documentation.

Reinforcement learning scaling enhances the reasoning capabilities of large language models, with reinforcement learning serving as the key technique to draw out complex reasoning. However, key technical details of state-of-the-art reasoning LLMs, such as those in the OpenAI O series, Claude 3 series, DeepMind's Gemini 2.5 series, and Grok 3 series, remain undisclosed, making it difficult for the research community to replicate their reinforcement learning training results. Therefore, we start our study from an Early Preview Reinforcement Learning (EPRLI) algorithm built on the open-source GRPO framework, incorporating difficulty-aware intervention for math problems. Applied to a 1.5B-parameter LLM, our method achieves 50.0% on AIME24, 89.2% on Math500, 77.1% on AMC, 35.3% on Minerva, and 51.9% on OBench, superpass O1-Preview and is comparable to O1-mini within standard school-lab settings.

The rise of Generative AI (GenAI) in recent years has catalyzed transformative advances in wireless communications and networks. Among the members of the GenAI family, Diffusion Models (DMs) have risen to prominence as a powerful option, capable of handling complex, high-dimensional data distribution, as well as consistent, noise-robust performance. In this survey, we aim to provide a comprehensive overview of the theoretical foundations and practical applications of DMs across future communication systems. We first provide an extensive tutorial of DMs and demonstrate how they can be applied to enhance optimizers, reinforcement learning and incentive mechanisms, which are popular approaches for problems in wireless networks. Then, we review and discuss the DM-based methods proposed for emerging issues in future networks and communications, including channel modeling and estimation, signal detection and data reconstruction, integrated sensing and communication, resource management in edge computing networks, semantic communications and other notable issues. We conclude the survey with highlighting technical limitations of DMs and their applications, as well as discussing future research directions.

Recent advances in generative models have highlighted the need for robust detectors capable of distinguishing real images from AI-generated images. While existing methods perform well on known generators, their performance often declines when tested with newly emerging or unseen generative models due to overlapping feature embeddings that hinder accurate cross-generator classification. In this paper, we propose Multimodal Discriminative Representation Learning for Generalizable AI-generated Image Detection (MiraGe), a method designed to learn generator-invariant features. Motivated by theoretical insights on intra-class variation minimization and inter-class separation, MiraGe tightly aligns features within the same class while maximizing separation between classes, enhancing feature discriminability. Moreover, we apply multimodal prompt learning to further refine these principles into CLIP, leveraging text embeddings as semantic anchors for effective discriminative representation learning, thereby improving generalizability. Comprehensive experiments across multiple benchmarks show that MiraGe achieves state-of-the-art performance, maintaining robustness even against unseen generators like Sora.

The rapid integration of generative artificial intelligence (AI) into postsecondary education and many other sectors resulted in a global reckoning with this new technology. This paper contributes to the study of the multifaceted influence of generative AI, with a particular focus on OpenAI's ChatGPT within academic settings during the first six months after the release in three specific ways . First, it scrutinize s the rise of ChatGPT as a transformative event construed through a study of mainstream discourses exhibiting AI hype. Second, i t discusses the perceived implications of generative AI for writing, teaching, and learning t hrough the lens of critical discourse analysis and critical AI studies . Third, i t encourages the necessity for best practices in the adoption of generative AI technologies in education.