Trump's ominous warning to Colombia as acting Venezuelan president issues message to world calling for'peace and dialogue, not war' Trump plans a military'quarantine' of Venezuela's oil to strong-arm Maduro's successor I got a GLP-1 drug with few questions asked... and never meeting a doctor face-to-face. But could that convenience have put my health at risk? Addicted, arrested and dead in a hotel corridor...Victoria Jones is the latest child of a famous parent to tragically spiral. So why ARE so many children of the rich and famous cursed? Marco Rubio'runs laps' around CBS reporter who asked why US commandos didn't nab Maduro associates in daring night time raid Prince Harry'desperately wants King Charles to come to Montecito and see Archie and Lilibet' Travis Kelce finally addresses possible retirement as Chiefs lose to NFL's worst team in what could be humiliating end to his iconic career State of Jennifer Garner and Jennifer Lopez's relationship revealed by insiders... as parents gossip about'less sociable' star at school play NASA's'queen of diamonds' EXPOSED: Genius is accused of treachery over top secret mission... as chilling details emerge Michael B. Jordan's unimpressed face sends fans wild as Timothee Chalamet cries on stage over Kylie Jenner North West, 12, sparks face piercing speculation after backlash over'risky' body modification'Out-of-touch' Gayle King slammed for complaining that her upper class seat doesn't have a window on her eight-hour flight'back to work' from Hawaii American family of seven stranded after Venezuela raids say they're trapped in a living hell... while oblivious influencers BOAST about getting stuck Ten people who spread false claims France's First Lady Brigitte Macron was born a man are found guilty of cyberbullying in Paris EXPOSED: The Air Force vet who let China steal America's nuclear secrets... and KEPT his $200K tax-funded salary From'sand theft auto' to space BABIES: The global innovations and trends set to shape 2026 From the rise of the humanoid robot to the weird world of AI girlfriends, 2025 had no shortage of strange and transformative inventions. Now, experts from the Nesta research foundation have revealed the global innovations and trends set to shape the world in 2026.

Large language models (LLMs) are being rapidly integrated into decision-support tools, automation workflows, and AI-enabled software systems. However, their behavior in production environments remains poorly understood, and their failure patterns differ fundamentally from those of traditional machine learning models. This paper presents a system-level taxonomy of fifteen hidden failure modes that arise in real-world LLM applications, including multi-step reasoning drift, latent inconsistency, context-boundary degradation, incorrect tool invocation, version drift, and cost-driven performance collapse. Using this taxonomy, we analyze the growing gap in evaluation and monitoring practices: existing benchmarks measure knowledge or reasoning but provide little insight into stability, reproducibility, drift, or workflow integration. We further examine the production challenges associated with deploying LLMs - including observability limitations, cost constraints, and update-induced regressions - and outline high-level design principles for building reliable, maintainable, and cost-aware LLM systems. Finally, we outline high-level design principles for building reliable, maintainable, and cost-aware LLM-based systems. By framing LLM reliability as a system-engineering problem rather than a purely model-centric one, this work provides an analytical foundation for future research on evaluation methodology, AI system robustness, and dependable LLM deployment.

Personalized AI systems, from recommendation systems to chatbots, are a prevalent method for distributing content to users based on their learned preferences. However, there is growing concern about the adverse effects of these systems, including their potential tendency to expose users to sensitive or harmful material, negatively impacting overall well-being. To address this concern quantitatively, it is necessary to create datasets with relevant sensitivity labels for content, enabling researchers to evaluate personalized systems beyond mere engagement metrics. To this end, we introduce two novel datasets that include a taxonomy of sensitivity labels alongside user-content ratings: one that integrates MovieLens rating data with content warnings from the Does the Dog Die? community ratings website, and another that combines fan-fiction interaction data and user-generated warnings from Archive of Our Own.

The widespread adoption and increasing prominence of large language models (LLMs) in global technologies necessitate a rigorous focus on ensuring their safety across a diverse range of linguistic and cultural contexts. The lack of a comprehensive evaluation and diverse data in existing multilingual safety evaluations for LLMs limits their effectiveness, hindering the development of robust multilingual safety alignment. To address this critical gap, we introduce LinguaSafe, a comprehensive multilingual safety benchmark crafted with meticulous attention to linguistic authenticity. The LinguaSafe dataset comprises 45k entries in 12 languages, ranging from Hungarian to Malay. Curated using a combination of translated, transcreated, and natively-sourced data, our dataset addresses the critical need for multilingual safety evaluations of LLMs, filling the void in the safety evaluation of LLMs across diverse under-represented languages from Hungarian to Malay. LinguaSafe presents a multidimensional and fine-grained evaluation framework, with direct and indirect safety assessments, including further evaluations for oversensitivity. The results of safety and helpfulness evaluations vary significantly across different domains and different languages, even in languages with similar resource levels. Our benchmark provides a comprehensive suite of metrics for in-depth safety evaluation, underscoring the critical importance of thoroughly assessing multilingual safety in LLMs to achieve more balanced safety alignment. Our dataset and code are released to the public to facilitate further research in the field of multilingual LLM safety.

Masked Face Recognition (MFR) is an increasingly important area in biometric recognition technologies, especially with the widespread use of masks as a result of the COVID-19 pandemic. This development has created new challenges for facial recognition systems due to the partial concealment of basic facial features. This paper aims to provide a comprehensive review of the latest developments in the field, with a focus on deep learning techniques, especially convolutional neural networks (CNNs) and twin networks (Siamese networks), which have played a pivotal role in improving the accuracy of covering face recognition. The paper discusses the most prominent challenges, which include changes in lighting, different facial positions, partial concealment, and the impact of mask types on the performance of systems. It also reviews advanced technologies developed to overcome these challenges, including data enhancement using artificial databases and multimedia methods to improve the ability of systems to generalize. In addition, the paper highlights advance in deep network design, feature extraction techniques, evaluation criteria, and data sets used in this area. Moreover, it reviews the various applications of masked face recognition in the fields of security and medicine, highlighting the growing importance of these systems in light of recurrent health crises and increasing security threats. Finally, the paper focuses on future research trends such as developing more efficient algorithms and integrating multimedia technologies to improve the performance of recognition systems in real-world environments and expand their applications.

Hate speech targeting children on social media is a serious and growing problem, yet current NLP systems struggle to detect it effectively. This gap exists mainly because existing datasets focus on adults, lack age specific labels, miss nuanced linguistic cues, and are often too small for robust modeling. To address this, we introduce ChildGuard, the first large scale English dataset dedicated to hate speech aimed at children. It contains 351,877 annotated examples from X (formerly Twitter), Reddit, and YouTube, labeled by three age groups: younger children (under 11), pre teens (11--12), and teens (13--17). The dataset is split into two subsets for fine grained analysis: a contextual subset (157K) focusing on discourse level features, and a lexical subset (194K) emphasizing word-level sentiment and vocabulary. Benchmarking state of the art hate speech models on ChildGuard reveals notable drops in performance, highlighting the challenges of detecting child directed hate speech.

Reasoning distillation has emerged as an effective approach to enhance the reasoning capabilities of smaller language models. However, the impact of large-scale reasoning distillation on other critical abilities, particularly in-context retrieval and reasoning, remains unexplored. This gap in understanding is particularly significant given the increasing importance of Retrieval-Augmented Generation (RAG) systems, where efficient acquisition and utilization of contextual information are paramount for generating reliable responses. Motivated by the need to understand how the extended long-CoT process influences long-context comprehension, we conduct a comprehensive investigation using a series of open-source models distilled from Deepseek-R1, renowned for its exceptional reasoning capabilities. Our study focuses on evaluating these models' performance in extracting and integrating relevant information from extended contexts through multi-document question and answering tasks. Through rigorous experimentation, we demonstrate that distilled reasoning patterns significantly improve long-context understanding. Our analysis reveals that distillation fosters greater long-context awareness by promoting more detailed and explicit reasoning processes during context analysis and information parsing. This advancement effectively mitigates the persistent "lost in the middle" issue that has hindered long-context models.

With the growing prominence of antibody - based therapeutics, antibody engineering has gained increasing attention as a critical area of research and development. Recent progress in transformer - based protein large language models (LLMs) has demonstrated prom ising applications in protein sequence design and structural prediction. Moreover, the availability of large - scale antibody datasets such as the Observed Antibody Space (OAS) database has opened new avenues for the development of LLMs specialized for proce ssing antibody sequences . Among these, RoBERTa has demonstrated improved performance relative to BERT, while maintaining a smaller parameter count (125M) compared to the BERT - based protein model, ProtBERT (420M). This reduced model size enables more efficient deployment in antibody - related application s . However, despite the numerous advantages of the RoBERTa architecture, antibody - specific foundational models built upon it have remained inaccessible to the research community. In this study, we introduce Ab - RoBERTa, a RoBERTa - based antibody - specific LLM, which is publicly available at https://huggingface.co/mogam - ai/Ab - RoBERTa . This resource is intended to support a wide range of antibody - related research applications including paratope prediction or humanness assessment .

With the widespread availability of pretrained Large Language Models (LLMs) and their training datasets, concerns about the security risks associated with their usage has increased significantly. One of these security risks is the threat of LLM poisoning attacks where an attacker modifies some part of the LLM training process to cause the LLM to behave in a malicious way. As an emerging area of research, the current frameworks and terminology for LLM poisoning attacks are derived from earlier classification poisoning literature and are not fully equipped for generative LLM settings. We conduct a systematic review of published LLM poisoning attacks to clarify the security implications and address inconsistencies in terminology across the literature. We propose a comprehensive poisoning threat model applicable to categorize a wide range of LLM poisoning attacks. The poisoning threat model includes four poisoning attack specifications that define the logistics and manipulation strategies of an attack as well as six poisoning metrics used to measure key characteristics of an attack. Under our proposed framework, we organize our discussion of published LLM poisoning literature along four critical dimensions of LLM poisoning attacks: concept poisons, stealthy poisons, persistent poisons, and poisons for unique tasks, to better understand the current landscape of security risks.

As large language models (LLMs) continue to be deployed and utilized across domains, the volume of LLM-generated data is growing rapidly. This trend highlights the increasing importance of effective and lossless compression for such data in modern text management systems. However, compressing LLM-generated data presents unique challenges compared to traditional human- or machine-generated content. Traditional machine-generated data is typically derived from computational processes or device outputs, often highly structured and limited to low-level elements like labels or numerical values. This structure enables conventional lossless compressors to perform efficiently. In contrast, LLM-generated data is more complex and diverse, requiring new approaches for effective compression. In this work, we conduct the first systematic investigation of lossless compression techniques tailored specifically to LLM-generated data. Notably, because LLMs are trained via next-token prediction, we find that LLM-generated data is highly predictable for the models themselves. This predictability enables LLMs to serve as efficient compressors of their own outputs. Through extensive experiments with 14 representative LLMs and 8 LLM-generated datasets from diverse domains, we show that LLM-based prediction methods achieve remarkable compression rates, exceeding 20x, far surpassing the 3x rate achieved by Gzip, a widely used general-purpose compressor. Furthermore, this advantage holds across different LLM sizes and dataset types, demonstrating the robustness and practicality of LLM-based methods in lossless text compression under generative AI workloads.