In 2005, cell phones were banned for the first time during the conclave, the process by which the Catholic Church elects its new pope. Twenty years later, after the death of Pope Francis, the election process is underway again. Authorities have two priorities: to protect the integrity of those attending the meeting, and to ensure that it proceeds in strict secrecy (under penalty of excommunication and imprisonment) until the final decision is made. By 2025, the Gendarmerie corps guarding Vatican City faces unprecedented technological challenges compared to other conclaves. Among them are artificial intelligence systems, drones, military satellites, microscopic microphones, a misinformation epidemic, and a world permanently connected and informed through social media.

Overnight Russian drone attacks on east, south and central Ukraine damaged civilian infrastructure and businesses in the Poltava region and injured civilians in the Odesa region, Ukrainian officials said early on Wednesday. Odesa came under a "massive attack" by Russian drones overnight on Tuesday, wounding at least three people, the head of the regional administration, Oleh Kiper, wrote on his Telegram page. A residential building in a densely populated urban area of Odesa, civilian infrastructure and an educational facility were hit, he said. Air defence units repelled Russian air attacks on the Kyiv region and Ukraine's second largest city of Kharkiv, regional governors said in posts on Telegram channels. Russian forces said they have retaken St Nicholas Belogorsky monastery in the village of Gornal in Russia's Kursk region, where Ukrainian troops had been based, Russia's TASS news agency quoted a security source as saying.

News data have become an essential resource across various disciplines, including economics, finance, management, social sciences, and computer science. Researchers leverage newspaper articles to study economic trends, market dynamics, corporate strategies, public perception, political discourse, and the evolution of public opinion. Additionally, news datasets have been instrumental in training large-scale language models, with applications in sentiment analysis, fake news detection, and automated news summarization. Despite their significance, access to comprehensive news corpora remains a key challenge. Many full-text news providers, such as Factiva and LexisNexis, require costly subscriptions, while free alternatives often suffer from incomplete data and transparency issues. This paper presents a novel approach to obtaining full-text newspaper articles at near-zero cost by leveraging data from the Global Database of Events, Language, and Tone (GDELT). Specifically, we focus on the GDELT Web News NGrams 3.0 dataset, which provides high-frequency updates of n-grams extracted from global online news sources. We provide Python code to reconstruct full-text articles from these n-grams by identifying overlapping textual fragments and intelligently merging them. Our method enables researchers to access structured, large-scale newspaper data for text analysis while overcoming the limitations of existing proprietary datasets. The proposed approach enhances the accessibility of news data for empirical research, facilitating applications in economic forecasting, computational social science, and natural language processing.

Multimodal aspect-based sentiment classification (MASC) is an emerging task due to an increase in user-generated multimodal content on social platforms, aimed at predicting sentiment polarity toward specific aspect targets (i.e., entities or attributes explicitly mentioned in text-image pairs). Despite extensive efforts and significant achievements in existing MASC, substantial gaps remain in understanding fine-grained visual content and the cognitive rationales derived from semantic content and impressions (cognitive interpretations of emotions evoked by image content). In this study, we present Chimera: a cognitive and aesthetic sentiment causality understanding framework to derive fine-grained holistic features of aspects and infer the fundamental drivers of sentiment expression from both semantic perspectives and affective-cognitive resonance (the synergistic effect between emotional responses and cognitive interpretations). Specifically, this framework first incorporates visual patch features for patch-word alignment. Meanwhile, it extracts coarse-grained visual features (e.g., overall image representation) and fine-grained visual regions (e.g., aspect-related regions) and translates them into corresponding textual descriptions (e.g., facial, aesthetic). Finally, we leverage the sentimental causes and impressions generated by a large language model (LLM) to enhance the model's awareness of sentimental cues evoked by semantic content and affective-cognitive resonance. Experimental results on standard MASC datasets demonstrate the effectiveness of the proposed model, which also exhibits greater flexibility to MASC compared to LLMs such as GPT-4o. We have publicly released the complete implementation and dataset at https://github.com/Xillv/Chimera

This paper introduces a novel multi-Agent framework that automates the end to end production of Qinqiang opera by integrating Large Language Models , visual generation, and Text to Speech synthesis. Three specialized agents collaborate in sequence: Agent1 uses an LLM to craft coherent, culturally grounded scripts;Agent2 employs visual generation models to render contextually accurate stage scenes; and Agent3 leverages TTS to produce synchronized, emotionally expressive vocal performances. In a case study on Dou E Yuan, the system achieved expert ratings of 3.8 for script fidelity, 3.5 for visual coherence, and 3.8 for speech accuracy-culminating in an overall score of 3.6, a 0.3 point improvement over a Single Agent baseline. Ablation experiments demonstrate that removing Agent2 or Agent3 leads to drops of 0.4 and 0.5 points, respectively, underscoring the value of modular collaboration. This work showcases how AI driven pipelines can streamline and scale the preservation of traditional performing arts, and points toward future enhancements in cross modal alignment, richer emotional nuance, and support for additional opera genres.

Recent advances in large language models (LLMs) have demonstrated strong performance on simple text classification tasks, frequently under zero-shot settings. However, their efficacy declines when tackling complex social media challenges such as propaganda detection, hateful meme classification, and toxicity identification. Much of the existing work has focused on using LLMs to generate synthetic training data, overlooking the potential of LLM-based text preprocessing and semantic augmentation. In this paper, we introduce an approach that prompts LLMs to clean noisy text and provide context-rich explanations, thereby enhancing training sets without substantial increases in data volume. We systematically evaluate on the SemEval 2024 multi-label Persuasive Meme dataset and further validate on the Google Jigsaw toxic comments and Facebook hateful memes datasets to assess generalizability. Our results reveal that zero-shot LLM classification underperforms on these high-context tasks compared to supervised models. In contrast, integrating LLM-based semantic augmentation yields performance on par with approaches that rely on human-annotated data, at a fraction of the cost. These findings underscore the importance of strategically incorporating LLMs into machine learning (ML) pipeline for social media classification tasks, offering broad implications for combating harmful content online.

As generative artificial intelligence (AI) enables the creation and dissemination of information at massive scale and speed, it is increasingly important to understand how people perceive AI-generated content. One prominent policy proposal requires explicitly labeling AI-generated content to increase transparency and encourage critical thinking about the information, but prior research has not yet tested the effects of such labels. To address this gap, we conducted a survey experiment (N=1601) on a diverse sample of Americans, presenting participants with an AI-generated message about several public policies (e.g., allowing colleges to pay student-athletes), randomly assigning whether participants were told the message was generated by (a) an expert AI model, (b) a human policy expert, or (c) no label. We found that messages were generally persuasive, influencing participants' views of the policies by 9.74 percentage points on average. However, while 94.6% of participants assigned to the AI and human label conditions believed the authorship labels, labels had no significant effects on participants' attitude change toward the policies, judgments of message accuracy, nor intentions to share the message with others. These patterns were robust across a variety of participant characteristics, including prior knowledge of the policy, prior experience with AI, political party, education level, or age. Taken together, these results imply that, while authorship labels would likely enhance transparency, they are unlikely to substantially affect the persuasiveness of the labeled content, highlighting the need for alternative strategies to address challenges posed by AI-generated information.

Since filmmaker George Lucas introduced audiences to the ways of the Jedi with Star Wars (now titled A New Hope) in 1977, the chronicles of that galaxy far, far away have grown to 11 movies, nine animated shows, five TV series, and a slew of non-canon shows, miniseries, video games, books, and other media. Even if you just stick to the canon stuff, it can be overwhelming, especially if you're trying to figure out how to watch Star Wars in order. But before we dive in, we'll emphasize that there really isn't a "correct" viewing order. There are several ways to enjoy the Star Wars universe as you proceed along your Jedi journey, and you may even be able to create your own method. The prequel trilogy dropped in the late 1990s and early 2000s, and the sequel trilogy hit theaters in the 2010s. Various standalone films were released intermittently throughout this timeline, offering fans opportunities to explore specific characters and events more deeply.

AI safety is a contentious topic. While some prominent figures of the AI community have argued that destructive general artificial intelligence (AI) is on the horizon, others derided their warning as a marketing stunt to sell large language models (LLMs). "If the call for'AI safety' is couched in terms of protecting humanity from rogue AIs, it very conveniently displaces accountability away from the corporations scaling harm in the name of profits," tweeted Emily Bender, a professor of computational linguistics at the University of Washington. Focusing on potential future harm from ever more powerful AI systems distracts from harm that is already happening today. Most of us do not set out to make software that is actively harmful.

The Washington Post is partnering with OpenAI to bring its reporting to ChatGPT. The two organizations did not disclose the financial terms of the agreement, but the deal will see ChatGPT display summaries, quotes and links to articles from The Post when users prompt the chatbot to search the web. "We're all in on meeting our audiences where they are," said Peter Elkins-Williams, head of global partnerships at The Post. "Ensuring ChatGPT users have our impactful reporting at their fingertips builds on our commitment to provide access where, how and when our audiences want it." The Post is no stranger to generative AI. In November, the publisher began using the technology to offer article summaries.