The broad adoption of Generative AI (GenAI) is impacting Computer Science education, and recent studies found its benefits and potential concerns when students use it for programming learning. However, most existing explorations focus on GenAI tools that primarily support text-to-text interaction. With recent developments, GenAI applications have begun supporting multiple modes of communication, known as multimodality. In this work, we explored how undergraduate programming novices choose and work with multimodal GenAI tools, and their criteria for choices. We selected a commercially available multimodal GenAI platform for interaction, as it supports multiple input and output modalities, including text, audio, image upload, and real-time screen-sharing. Through 16 think-aloud sessions that combined participant observation with follow-up semi-structured interviews, we investigated student modality choices for GenAI tools when completing programming problems and the underlying criteria for modality selections. With multimodal communication emerging as the future of AI in education, this work aims to spark continued exploration on understanding student interaction with multimodal GenAI in the context of CS education.

As large language models (LLMs) continue to be integrated into modern technology, there has been an increased push towards code generation applications, which also naturally extends to hardware design automation. LLM-based solutions for register transfer level (RTL) code generation for intellectual property (IP) designs have grown, especially with fine-tuned LLMs, prompt engineering, and agentic approaches becoming popular in literature. However, a gap has been exposed in these techniques, as they fail to integrate novel IPs into the model's knowledge base, subsequently resulting in poorly generated code. Additionally, as general-purpose LLMs continue to improve, fine-tuned methods on older models will not be able to compete to produce more accurate and efficient designs. Although some retrieval augmented generation (RAG) techniques exist to mitigate challenges presented in fine-tuning approaches, works tend to leverage low-quality codebases, incorporate computationally expensive fine-tuning in the frameworks, or do not use RAG directly in the RTL generation step. In this work, we introduce DeepV: a model-agnostic RAG framework to generate RTL designs by enhancing context through a large, high-quality dataset without any RTL-specific training. Our framework benefits the latest commercial LLM, OpenAI's GPT-5, with a near 17% increase in performance on the VerilogEval benchmark. We host DeepV for use by the community in a Hugging Face (HF) Space: https://huggingface.co/spaces/FICS-LLM/DeepV.

Deepfakes, synthetic media created using advanced AI techniques, pose a growing threat to information integrity, particularly in politically sensitive contexts. This challenge is amplified by the increasing realism of modern generative models, which our human perception study confirms are often indistinguishable from real images. Yet, existing deepfake detection benchmarks rely on outdated generators or narrowly scoped datasets (e.g., single-face imagery), limiting their utility for real-world detection. To address these gaps, we present OpenFake, a large politically grounded dataset specifically crafted for benchmarking against modern generative models with high realism, and designed to remain extensible through an innovative crowdsourced adversarial platform that continually integrates new hard examples. OpenFake comprises nearly four million total images: three million real images paired with descriptive captions and almost one million synthetic counterparts from state-of-the-art proprietary and open-source models. Detectors trained on OpenFake achieve near-perfect in-distribution performance, strong generalization to unseen generators, and high accuracy on a curated in-the-wild social media test set, significantly outperforming models trained on existing datasets. Overall, we demonstrate that with high-quality and continually updated benchmarks, automatic deepfake detection is both feasible and effective in real-world settings.

OpenAI revealed last week the custom AI tools it uses internally. The news sent some software companies into turmoil. Allan Thygesen, the CEO of Docusign, was not particularly concerned when he saw the news last week that OpenAI had created an internal tool called DocuGPT . He might have preferred that OpenAI choose a different name for its contracting tool. But still, he thought, DocuGPT barely scratched the surface of what Docusign can do.

MrBeast: AI means it's'scary times' for YouTube creators The world's biggest YouTuber, MrBeast, says the rapid advance of generative artificial intelligence (AI) is scary for the millions of creators currently making content for a living. AI tools that can create fully-formed videos from simple text prompts by users have made rapid advances in recent years. On social media, MrBeast, real name Jimmy Donaldson, asked what would happen to people like him when AI videos are just as good as normal videos. Fears about the impact AI will have on the jobs market are widespread - but particularly acute in the creative industries. In the film and video game industries, there has been extensive industrial action over the use of AI.

In this still from the Sora 2 promotional video, an ai-generated cameo of Sam Altman shows us through worlds of generated content. Last week OpenAI released Sora, a TikTok-style app that presents an endless feed of exclusively AI-generated videos, each up to 10 seconds long. The app allows you to create a "cameo" of yourself--a hyperrealistic avatar that mimics your appearance and voice--and insert other peoples' cameos into your own videos (depending on what permissions they set). To some people who believed earnestly in OpenAI's promise to build AI that benefits all of humanity, the app is a punchline. A former OpenAI researcher who left to build an AI-for-science startup referred to Sora as an "infinite AI tiktok slop machine." That hasn't stopped it from soaring to the top spot on Apple's US App Store.

OpenAI has rolled out a social app powered by Sora 2, its artificial intelligence video generator, which was quickly flooded with videos featuring iconic Japanese intellectual property. In a short video widely shared online, Pokemon frolic through a lush green field while OpenAI CEO Sam Altman watches from the sidelines. He then turns to the camera and says, "I hope Nintendo doesn't sue us." Named for the Japanese word for "sky" due to the product's "limitless potential," according to company lore, the platform was released to a handful of users last week and was quickly flooded with videos featuring iconic Japanese intellectual property (IP), including Pokemon, One Piece and Dragon Ball Z. Such videos, which are only possible to generate because of OpenAI "training" Sora 2 on the work of human creators, have been widely branded "AI slop" by critics.

Jony Ive Says He Wants His OpenAI Devices to'Make Us Happy' "I don't think we have an easy relationship with our technology at the moment," the former Apple designer said at OpenAI's developer conference in San Francisco on Monday. At OpenAI's developer conference in San Francisco on Monday, CEO Sam Altman and ex-Apple designer Jony Ive spoke in vague terms about the "family of devices" the pair are currently working to develop . "As great as phones and computers are, there's something new to do," Altman said on stage with Ive. The duo confirmed that OpenAI is working on more than one hardware product but finer details, ranging from use cases to to specifications, remain under wraps. Figuring out new computing form factors is hard," said Altman in a media briefing earlier in the day. "I think we have a chance to do something amazing, but it will take a while." Ive said that his team has generated "15 to 20 really compelling product" ideas on the journey to find the right kind of ...

Deep generative models have made significant advances in generating complex content, yet conditional generation remains a fundamental challenge. Existing conditional generative adversarial networks often struggle to balance the dual objectives of assessing authenticity and conditional alignment of input samples within their conditional discriminators. To address this, we propose a novel discriminator design that integrates three key capabilities: unconditional discrimination, matching-aware supervision to enhance alignment sensitivity, and adaptive weighting to dynamically balance all objectives. Specifically, we introduce Sum of Naturalness and Alignment (SONA), which employs separate projections for naturalness (authenticity) and alignment in the final layer with an inductive bias, supported by dedicated objective functions and an adaptive weighting mechanism. Extensive experiments on class-conditional generation tasks show that \ours achieves superior sample quality and conditional alignment compared to state-of-the-art methods. Furthermore, we demonstrate its effectiveness in text-to-image generation, confirming the versatility and robustness of our approach.

With many endangered languages at risk of disappearing, efforts to preserve them now rely more than ever on using technology alongside culturally informed teaching strategies. This study examines user behaviors in TALKA, a generative AI-powered chatbot designed for Hakka language engagement, by employing a dual-layered analytical framework grounded in Bloom's Taxonomy of cognitive processes and dialogue act categorization. We analyzed 7,077 user utterances, each carefully annotated according to six cognitive levels and eleven dialogue act types. These included a variety of functions, such as asking for information, requesting translations, making cultural inquiries, and using language creatively. Pragmatic classifications further highlight how different types of dialogue acts--such as feedback, control commands, and social greetings--align with specific cognitive intentions. The results suggest that generative AI chatbots can support language learning in meaningful ways--especially when they are designed with an understanding of how users think and communicate. They may also help learners express themselves more confidently and connect with their cultural identity. The TALKA case provides empirical insights into how AI-mediated dialogue facilitates cognitive development in low-resource language learners, as well as pragmatic negotiation and socio-cultural affiliation. By focusing on AI-assisted language learning, this study offers new insights into how technology can support language preservation and educational practice.