Although text-to-image generation is rapidly advancing, these AI models are mostly English-centric. Researchers at the University of Amsterdam Faculty of Science have created NeoBabel, an AI image generator that can work in six different languages. By making all elements of their research open source, anyone can build on the model and help push inclusive AI research. When you generate an image with AI, the results are often better when your prompt is in English. This is because many AI models are English at their core: if you use another language, your prompt is translated into English before the image is created.

Warning: this paper contains example data that may be offensive or harmful.

Although wolf-canine interbreeding has been considered extremely rare, the latest research shows that many present-day canines carry a small amount of wolf genes. A surprising study reveals that there is a trace of wolf lurking within the tiny body of a Chihuahua and the gigantic build of a St. Bernard. An international research team from the American Museum of Natural History and the National Museum of Natural History analyzed the genomes of 2,693 dogs and wolves and found that 64.1 percent of purebred dogs carry fragments of wolf DNA. Furthermore, a study of village dogs (free-roaming dogs living in or near human communities) from around the world found genetic traces of wolves in all 280 analyzed pups. Dogs are thought to have evolved from populations of gray wolves, which became extinct during the Late Pleistocene epoch about 20,000 years ago.

Data Driven Classroom Interviews (DDCIs) are an interviewing technique that is facilitated by recent technological developments in the learning analytics community. DDCIs are short, targeted interviews that allow researchers to contextualize students' interactions with a digital learning environment (e.g., intelligent tutoring systems or educational games) while minimizing the amount of time that the researcher interrupts that learning experience, and focusing researcher time on the events they most want to focus on DDCIs are facilitated by a research tool called the Quick Red Fox (QRF)--an open-source server-client Android app that optimizes researcher time by directing interviewers to users that have just displayed an interesting behavior (previously defined by the research team). QRF integrates with existing student modeling technologies (e.g., behavior-sensing, affect-sensing, detection of self-regulated learning) to alert researchers to key moments in a learner's experience. This manual documents the tech while providing training on the processes involved in developing triggers and interview techniques; it also suggests methods of analyses.

Plausible, but inaccurate, tokens in model-generated text are widely believed to be pervasive and problematic for the responsible adoption of language models. Despite this concern, there is little scientific work that attempts to measure the prevalence of language model hallucination in a comprehensive way. In this paper, we argue that language models should be evaluated using repeatable, open, and domain-contextualized hallucination benchmarking. We present a taxonomy of hallucinations alongside a case study that demonstrates that when experts are absent from the early stages of data creation, the resulting hallucination metrics lack validity and practical utility.

Warning: this paper contains example data that may be offensive or harmful.

"AI" tools are all the rage at the moment, even among users who aren't all that savvy when it comes to conventional software or security--and that's opening up all sorts of new opportunities for hackers and others who want to take advantage of them. A new research team has discovered a way to hide prompt injection attacks in uploaded images. A prompt injection attack is a way to hide instructions for an LLM or other "artificial intelligence" system, usually somewhere a human operator can't see them. It's the whispered "loser-says-what" of computer security. A great example is hiding a phishing attempt in an email in plain text that's colored the same as the background, knowing that Gemini will summarize the text even though the human recipient can't read it.

In financial trading, large language model (LLM)-based agents demonstrate significant potential. However, the high sensitivity to market noise undermines the performance of LLM-based trading systems. To address this limitation, we propose a novel multi-agent system featuring an internal competitive mechanism inspired by modern corporate management structures. The system consists of two specialized teams: (1) Data Team - responsible for processing and condensing massive market data into diversified text factors, ensuring they fit the model's constrained context. (2) Research Team - tasked with making parallelized multipath trading decisions based on deep research methods. The core innovation lies in implementing a real-time evaluation and ranking mechanism within each team, driven by authentic market feedback. Each agent's performance undergoes continuous scoring and ranking, with only outputs from top-performing agents being adopted. The design enables the system to adaptively adjust to dynamic environment, enhances robustness against market noise and ultimately delivers superior trading performance. Experimental results demonstrate that our proposed system significantly outperforms prevailing multi-agent systems and traditional quantitative investment methods across diverse evaluation metrics. ContestTrade is open-sourced on GitHub at https://github.com/FinStep-AI/ContestTrade.