Language is a fundamental aspect of human societies, continuously evolving in response to various stimuli, including societal changes and intercultural interactions. Technological advancements have profoundly transformed communication, with social media emerging as a pivotal force that merges entertainment-driven content with complex social dynamics. As these platforms reshape public discourse, analyzing the linguistic features of user-generated content is essential to understanding their broader societal impact. In this paper, we examine the linguistic complexity of content produced by influential users on Twitter across three globally significant and contested topics: COVID-19, COP26, and the Russia-Ukraine war. By combining multiple measures of textual complexity, we assess how language use varies along four key dimensions: account type, political leaning, content reliability, and sentiment. Our analysis reveals significant differences across all four axes, including variations in language complexity between individuals and organizations, between profiles with sided versus moderate political views, and between those associated with higher versus lower reliability scores. Additionally, profiles producing more negative and offensive content tend to use more complex language, with users sharing similar political stances and reliability levels converging toward a common jargon. Our findings offer new insights into the sociolinguistic dynamics of digital platforms and contribute to a deeper understanding of how language reflects ideological and social structures in online spaces.

Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during pre-training, recent studies employ multi-task learning, in which shared message passing layers initially process input atomistic structures regardless of source, then route them to multiple decoding heads that predict data-specific outputs. This approach stabilizes pre-training and enhances a model's transferability to unexplored chemical regions. Preliminary results on approximately four million structures are encouraging, yet questions remain about generaliz-ability to larger, more diverse datasets and scalability on supercomputers. We propose a multi-task parallelism method that distributes each head across computing resources with GPU acceleration. Implemented in the open-source HydraGNN architecture, our method was trained on over 24 million structures from five datasets and tested on the Perlmut-ter, Aurora, and Frontier supercomputers, demonstrating efficient scaling on all three highly heterogeneous super-computing architectures. Keywords: Graph Neural Networks Distributed Data Parallelism Model Parallelism Multi-Fidelity Data Atomistic Modeling.

--Large Language Models (LLMs) have recently emerged as powerful tools for natural language generation, with applications spanning from content creation to social simulations. Their ability to mimic human interactions raises both opportunities and concerns, particularly in the context of politically relevant online discussions. In this study, we evaluate the performance of LLMs in replicating user-generated content within a real-world, divisive scenario: Reddit conversations during the 2016 US Presidential election. In particular, we conduct three different experiments, asking GPT -4 to generate comments by impersonating either real or artificial partisan users. We analyze the generated comments in terms of political alignment, sentiment, and linguistic features, comparing them against real user contributions and benchmarking against a null model. We find that GPT -4 is able to produce realistic comments, both in favor of or against the candidate supported by the community, yet tending to create consensus more easily than dissent. In addition we show that real and artificial comments are well separated in a semantically embedded space, although they are indistinguishable by manual inspection. Our findings provide insights on the potential use of LLMs to sneak into online discussions, influence political debate and shape political narratives, bearing broader implications of AI-driven discourse manipulation. Artificial intelligence (AI) has been the cornerstone of scientific inquiry and technological advancement for several decades, driving innovation in multiple scientific fields [1]. Despite its long-standing presence, AI has captured unprecedented public and academic attention in recent years, largely due to breakthroughs in generative models [2]. Among these, Large Language Models (LLMs) [3] stand out as a transforma-tive innovation, redefining how we approach problems in natural language processing, decision-making, and simulations. In the past two years, the release of powerful models capable of generating coherent and contextually relevant responses (such as GPT -3.5 and GPT -4 [4], Llama [5], Mistral [6] and Gemini [7]) not only captivated the public imagination, but also opened new avenues for research in complex systems [8]-[10]. In particular, LLMs have sparked a lot of interest in complex networks studies and Agent-Based models (ABM). For example, a population of interacting LLMs agents was shown to exhibit preferential attachment [11] and thus creating scale-free networks [12], a characteristic found in many real-world systems [13].

Human smuggling networks are increasingly adaptive and difficult to analyze. Legal case documents offer valuable insights but are unstructured, lexically dense, and filled with ambiguous or shifting references-posing challenges for automated knowledge graph (KG) construction. Existing KG methods often rely on static templates and lack coreference resolution, while recent LLM-based approaches frequently produce noisy, fragmented graphs due to hallucinations, and duplicate nodes caused by a lack of guided extraction. We propose CORE-KG, a modular framework for building interpretable KGs from legal texts. It uses a two-step pipeline: (1) type-aware coreference resolution via sequential, structured LLM prompts, and (2) entity and relationship extraction using domain-guided instructions, built on an adapted GraphRAG framework. CORE-KG reduces node duplication by 33.28%, and legal noise by 38.37% compared to a GraphRAG-based baseline-resulting in cleaner and more coherent graph structures. These improvements make CORE-KG a strong foundation for analyzing complex criminal networks.

Evaluation benchmark characteristics may distort the true benefits of domain adaptation in retrieval models. This creates misleading assessments that influence deployment decisions in specialized domains. We show that two benchmarks with drastically different features such as topic diversity, boundary overlap, and semantic complexity can influence the perceived benefits of fine-tuning. Using environmental regulatory document retrieval as a case study, we fine-tune ColBERTv2 model on Environmental Impact Statements (EIS) from federal agencies. We evaluate these models across two benchmarks with different semantic structures. Our findings reveal that identical domain adaptation approaches show very different perceived benefits depending on evaluation methodology. On one benchmark, with clearly separated topic boundaries, domain adaptation shows small improvements (maximum 0.61% NDCG gain). However, on the other benchmark with overlapping semantic structures, the same models demonstrate large improvements (up to 2.22% NDCG gain), a 3.6-fold difference in the performance benefit. We compare these benchmarks through topic diversity metrics, finding that the higher-performing benchmark shows 11% higher average cosine distances between contexts and 23% lower silhouette scores, directly contributing to the observed performance difference. These results demonstrate that benchmark selection strongly determines assessments of retrieval system effectiveness in specialized domains. Evaluation frameworks with well-separated topics regularly underestimate domain adaptation benefits, while those with overlapping semantic boundaries reveal improvements that better reflect real-world regulatory document complexity. Our findings have important implications for developing and deploying AI systems for interdisciplinary domains that integrate multiple topics.

Ukraine has lost an F-16 aircraft and its pilot while repelling a Russian missile and drone strike, according to the war-torn country's air force. After shooting down seven air targets, the plane was damaged and lost altitude overnight, the Ukrainian military said in a statement published on Telegram on Sunday. "This night, while repelling a massive enemy air attack, a pilot of the 1st class, Lieutenant Colonel Maksym Ustimenko, born in 1993, died on an F-16 aircraft," it said. In a separate statement, the air force said Russia launched 537 projectiles against Ukraine, including Shahed drones, cruise and ballistic missiles. Ukraine claimed to have intercepted 475 of them.

A Russian drone attack killed a teacher and her husband in Ukraine's Odesa, and wounded 14 others, according to Ukrainian officials. Three of the victims, including a child, were in critical condition. At least two others were killed in another Russian attack on the villages of Kostiantynivka and Ivanopillia in the eastern region of Donetsk on Friday, according to Governor Vadym Filashkin. Explosions were heard in the Ukrainian capital, Kyiv, on Saturday night, with Mayor Vitali Klitschko warning residents to take shelter from Russian drones "heading for the city", according to the official Ukrinform news agency. Russia's Ministry of Defence said Russian forces have taken control of the settlement of Chervona Zirka in Donetsk.

Israeli attacks on southern Lebanon on multiple vehicles have killed three people as attacks continue despite a November ceasefire with the armed group Hezbollah. Lebanon's Ministry of Public Health said on Saturday that one person was killed in an "Israeli enemy" drone strike on a car in the village of Kunin while two others were killed after an Israeli strike on a motorcycle in Mahrouna, near Tyre. The Israeli army claimed that the attack on the car "eliminated the terrorist Hassan Muhammad Hammoudi", who it said was responsible for antitank missile attacks on Israeli territory during the recent war. The latest Israeli attacks came a day after Israel killed a woman and wounded 25 people in attacks across southern Lebanon. Lebanon's National News Agency reported that the woman was killed in an Israeli drone strike on an apartment in the city of Nabatieh.

Ukraine said it carried out an overnight drone strike on the Kirovske airfield in Crimea and claimed that multiple Russian helicopters and an air defence system were destroyed in the strike. According to a Ukraine Security Service (SBU) statement, the drones targeted areas where Russian aviation units, air defence assets, ammunition depots and unmanned aerial vehicles were located. The agency claimed that Mi-8, Mi-26, and Mi-28 helicopters, as well as a Pantsir-S1 missile and gun system were destroyed. "Secondary detonations continued throughout the night at the airfield," the SBU said, calling the strike part of broader efforts to disrupt Russian aerial operations. "The enemy must understand that expensive military equipment and ammunition are not safe anywhere – not on the line of contact, not in Crimea, and not deep in the rear."

WIRED published a shocking investigation this week based on records, including audio recordings, of hundreds of emergency calls from United States Immigration and Customs Enforcement (ICE) detention centers. The calls--which include reports of incidents of staff sexual assaults, suicide attempts, and head injuries--indicate a system inundated by life-threatening incidents, delayed treatment, and overcrowding. In a 6-3 decision on Friday, the US Supreme Court upheld a Texas porn ID law, finding that age verification for explicit sites is constitutional. In a dissent, Justice Elena Kagan warned that this determination ignores First Amendment precedent and will have privacy implications for adults. Looking at the US bombing of Iranian nuclear sites last weekend, President Donald Trump posted initial announcements of the strikes on the social Network Truth Social, which then began suffering intermittent outages.