Pope Leo XIV made his first address to the College of Cardinals on May 10, 2025 in Vatican City, and touched upon the rise of artificial intelligence. Pope Leo XIV made his first address to the College of Cardinals on May 10, 2025 in Vatican City, and touched upon the rise of artificial intelligence. As technologists race to accelerate AI's progress with minimal guardrails, they are being met with increasing resistance from a powerful global contingent: Christian leaders and their congregations. Christians are not a monolith by any means. But this year, Christian leaders across sects--including Catholics, Evangelicals, and Baptists--sounded the alarm on AI's potential impact on family, human relationships, labor, and the church itself.

The rapid progress of Large Language Models (LLMs) has transformed natural language processing and broadened its impact across research and society. Yet, systematic evaluation of these models, especially for languages beyond English, remains limited. "Challenging the Abilities of LAnguage Models in ITAlian" (CALAMITA) is a large-scale collaborative benchmarking initiative for Italian, coordinated under the Italian Association for Computational Linguistics. Unlike existing efforts that focus on leaderboards, CALAMITA foregrounds methodology: it federates more than 80 contributors from academia, industry, and the public sector to design, document, and evaluate a diverse collection of tasks, covering linguistic competence, commonsense reasoning, factual consistency, fairness, summarization, translation, and code generation. Through this process, we not only assembled a benchmark of over 20 tasks and almost 100 subtasks, but also established a centralized evaluation pipeline that supports heterogeneous datasets and metrics. We report results for four open-weight LLMs, highlighting systematic strengths and weaknesses across abilities, as well as challenges in task-specific evaluation. Beyond quantitative results, CALAMITA exposes methodological lessons: the necessity of fine-grained, task-representative metrics, the importance of harmonized pipelines, and the benefits and limitations of broad community engagement. CALAMITA is conceived as a rolling benchmark, enabling continuous integration of new tasks and models. This makes it both a resource -- the most comprehensive and diverse benchmark for Italian to date -- and a framework for sustainable, community-driven evaluation. We argue that this combination offers a blueprint for other languages and communities seeking inclusive and rigorous LLM evaluation practices.

We investigate reinforcement learning in the Game Of Hidden Rules (GOHR) environment, a complex puzzle in which an agent must infer and execute hidden rules to clear a 6$\times$6 board by placing game pieces into buckets. We explore two state representation strategies, namely Feature-Centric (FC) and Object-Centric (OC), and employ a Transformer-based Advantage Actor-Critic (A2C) algorithm for training. The agent has access only to partial observations and must simultaneously infer the governing rule and learn the optimal policy through experience. We evaluate our models across multiple rule-based and trial-list-based experimental setups, analyzing transfer effects and the impact of representation on learning efficiency.

In this paper, we first present a novel way of computationally analysing and extracting illocutionary forces from dialogue using Bert-based Large Language Models, and demonstrate how these features impact the response of a conversational agent guided by a document-based knowledge bank demonstrated by a bespoke web conversational chat agent system developed. Our proposed illocutionary force extraction and classification technique is the first of its kind using the Argument Interchange Format (AIF) Dataset, showing an improved performance compared to two methods for carrying out similar tasks with a macro F1 of approximately 45%. When we evaluated the system based on 2 knowledge files, with 2 user queries each, across 5 open-source large language models (LLMs) using 10 standard metrics we found out that larger open-source models, such as Llama2:13b and Llama3-chatqa-latest, demonstrated an improved alignment when the user illocutionary force was included with their query, achieving higher QA and linguistic similarity scores. The smaller models on the other hand like Tinyllama:latest showed an increased perplexity and mixed performance, which explicitly indicated struggles in processing queries that explicitly included illocutionary forces. The results from the analysis highlight the potential of illocutionary force to enhance conversational depth while underscoring the need for model-specific optimizations to address increased computational costs and response times.

It has been established in recent work that Large Language Models (LLMs) can be prompted to "self-play" conversational games that probe certain capabilities (general instruction following, strategic goal orientation, language understanding abilities), where the resulting interactive game play can be automatically scored. In this paper, we take one of the proposed frameworks for setting up such game-play environments, and further test its usefulness as an evaluation instrument, along a number of dimensions: We show that it can easily keep up with new developments while avoiding data contamination, we show that the tests implemented within it are not yet saturated (human performance is substantially higher than that of even the best models), and we show that it lends itself to investigating additional questions, such as the impact of the prompting language on performance. We believe that the approach forms a good basis for making decisions on model choice for building applied interactive systems, and perhaps ultimately setting up a closed-loop development environment of system and simulated evaluator.

It took Ralph Ellison seven years to write Invisible Man. It took J. D. Salinger about 10 to write The Catcher in the Rye. J. K. Rowling spent at least five years on the first Harry Potter book. Writing with the hope of publishing is always a leap of faith. Will you finish the project?

Communication success relies heavily on reading participants' reactions. Such feedback is especially important for mental health counselors, who must carefully consider the client's progress and adjust their approach accordingly. However, previous NLP research on counseling has mainly focused on studying counselors' intervention strategies rather than their clients' reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encompasses counselors' strategies and client reaction behaviors. The framework has been tested against a large-scale, high-quality text-based counseling dataset we collected over the past two years from an online welfare counseling platform. Our study shows how clients react to counselors' strategies, how such reactions affect the final counseling outcomes, and how counselors can adjust their strategies in response to these reactions. We also demonstrate that this study can help counselors automatically predict their clients' states.

The rise of AI technology has brought about many changes in various industries, and photography is no exception. With AI photography, the line between what is real and what is fake has become increasingly blurred. This has sparked a debate on the significance of authenticity in photography and the impact that AI technology has on the art form. Artificial intelligence is being used to enhance photos, manipulate images, and even create entirely new images from scratch. This technology can produce highly realistic images, making it difficult to distinguish between what was captured in a camera and what was created in a computer. While AI photography has the potential to revolutionize the industry and open up new possibilities for artists, it also raises questions about the ethics of using AI in photography.

Capsule neural networks replace simple, scalar-valued neurons with vector-valued capsules. They are motivated by the pattern recognition system in the human brain, where complex objects are decomposed into a hierarchy of simpler object parts. Such a hierarchy is referred to as a parse-tree. Conceptually, capsule neural networks have been defined to realize such parse-trees. The capsule neural network (CapsNet), by Sabour, Frosst, and Hinton, is the first actual implementation of the conceptual idea of capsule neural networks. CapsNets achieved state-of-the-art performance on simple image recognition tasks with fewer parameters and greater robustness to affine transformations than comparable approaches. This sparked extensive follow-up research. However, despite major efforts, no work was able to scale the CapsNet architecture to more reasonable-sized datasets. Here, we provide a reason for this failure and argue that it is most likely not possible to scale CapsNets beyond toy examples. In particular, we show that the concept of a parse-tree, the main idea behind capsule neuronal networks, is not present in CapsNets. We also show theoretically and experimentally that CapsNets suffer from a vanishing gradient problem that results in the starvation of many capsules during training.

In just a few years, the number of artworks produced by self-described AI artists has dramatically increased. Some of these works have been sold by large auction houses for dizzying prices and have found their way into prestigious curated collections. Initially spearheaded by a few technologically knowledgeable artists who adopted computer programming as part of their creative process, AI art has recently been embraced by the masses, as image generation technology has become both more effective and easier to use without coding skills. The AI art movement rides on the coattails of technical progress in computer vision, a research area dedicated to designing algorithms that can process meaningful visual information. A subclass of computer vision algorithms, called generative models, occupies center stage in this story.