Charlie Kirk dead at 31: What we know so far about MAGA star's death at Utah campus that sent shockwaves around the world as FBI botches arrest and Trump promises ultimate punishment MSNBC analyst Matthew Dowd fired over'disgusting' on-air comments about Charlie Kirk shortly after conservative star was assassinated Elite sniper breaks down Charlie Kirk assassin's sick plot... and reveals tiny detail everyone's missed: The gun. MAUREEN CALLAHAN: Charlie Kirk's body wasn't even cold... before the fighting started again. Do these ghouls not see where this is headed? Charlie Kirk's powerful tribute to murdered Ukrainian refugee hours before his own assassination: 'America will never be the same' Musk dethroned as richest person by forgotten Wall Street darling's founder as stock soars 42% Trump issues Oval Office address over Charlie Kirk's assassination: 'This is a dark moment for America' TMZ forced to apologize after staff heard erupting in laughter as Charlie Kirk's death was announced Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season America's top banker Jamie Dimon makes chilling warning that economy is struggling Fierce debate erupts over'non-human' technology in space after video captures UFO surviving Hellfire strike Is this Charlie Kirk's killer? This Oscar-nominated actress, 68, will soon reunite with her ex in Spain for their daughter's wedding, can you guess who?

FBI under pressure over open airport five miles from Charlie Kirk assassination hit as private jet'vanishes' after shooting MSNBC analyst Matthew Dowd fired over'disgusting' on-air comments about Charlie Kirk shortly after conservative star was assassinated Elite sniper breaks down Charlie Kirk assassin's sick plot... and reveals tiny detail everyone's missed: The gun. MAUREEN CALLAHAN: Charlie Kirk's body wasn't even cold... before the fighting started again. Do these ghouls not see where this is headed? Charlie Kirk's powerful tribute to murdered Ukrainian refugee hours before his own assassination: 'America will never be the same' Musk dethroned as richest person by forgotten Wall Street darling's founder as stock soars 42% Charlie Kirk dead at 31: What we know so far about MAGA star's death at Utah campus that sent shockwaves around the world as FBI botches arrest and Trump promises ultimate punishment TMZ forced to apologize after staff heard erupting in laughter as Charlie Kirk's death was announced Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season Trump issues Oval Office address over Charlie Kirk's assassination: 'This is a dark moment for America' Fierce debate erupts over'non-human' technology in space after video captures UFO surviving Hellfire strike Is this Charlie Kirk's killer? This Oscar-nominated actress, 68, will soon reunite with her ex in Spain for their daughter's wedding, can you guess who?

A legal battle is brewing, as two companies are suing to overturn the two-year ban. Last week, a legal battle over lab-grown meat kicked off in Texas. On September 1, a two-year ban on the technology went into effect across the state; the following day, two companies filed a lawsuit against state officials. The two companies, Wildtype Foods and Upside Foods, are part of a growing industry that aims to bring new types of food to people's plates. These products, often called cultivated meat by the industry, take live animal cells and grow them in the lab to make food products without the need to slaughter animals. Here's what we know about lab-grown meat and climate change Cultivated meat is coming to the US.

FBI under pressure over open airport five miles from Charlie Kirk assassination hit as private jet'vanishes' after shooting MSNBC analyst Matthew Dowd fired over'disgusting' on-air comments about Charlie Kirk shortly after conservative star was assassinated Elite sniper breaks down Charlie Kirk assassin's sick plot... and reveals tiny detail everyone's missed: The gun. MAUREEN CALLAHAN: Charlie Kirk's body wasn't even cold... before the fighting started again. Do these ghouls not see where this is headed? Charlie Kirk's powerful tribute to murdered Ukrainian refugee hours before his own assassination: 'America will never be the same' Musk dethroned as richest person by forgotten Wall Street darling's founder as stock soars 42% Charlie Kirk dead at 31: What we know so far about MAGA star's death at Utah campus that sent shockwaves around the world as FBI botches arrest and Trump promises ultimate punishment TMZ forced to apologize after staff heard erupting in laughter as Charlie Kirk's death was announced Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season Trump issues Oval Office address over Charlie Kirk's assassination: 'This is a dark moment for America' Fierce debate erupts over'non-human' technology in space after video captures UFO surviving Hellfire strike Is this Charlie Kirk's killer? This Oscar-nominated actress, 68, will soon reunite with her ex in Spain for their daughter's wedding, can you guess who?

'War Is Here': The Far-Right Responds to Charlie Kirk Shooting With Calls for Violence Prominent far-right figures and elected officials have called for vengeance following the death of conservative activist Charlie Kirk. Conservative political activist Charlie Kirk, founder of Turning Point Action, speaks during a meeting on the campus of the University of Arizona in Tucson on October 17, 2024. Minutes after right-wing activist Charlie Kirk was fatally shot while speaking at an event in Utah on Wednesday, far-right influencers and extremist communities lit up social media with calls for violence against the left. Kirk, the cofounder of the conservative youth organizing group Turning Point USA, was shot and killed while taking questions at a TPUSA event held at Utah Valley University. Law enforcement officials said late afternoon Wednesday that a "person of interest" was in custody, but that individual was later released.

Ensuring equitable treatment (fairness) across protected attributes (such as gender or ethnicity) is a critical issue in machine learning. Most existing literature focuses on binary classification, but achieving fairness in regression tasks-such as insurance pricing or hiring score assessments-is equally important. Moreover, anti-discrimination laws also apply to continuous attributes, such as age, for which many existing methods are not applicable. In practice, multiple protected attributes can exist simultaneously; however, methods targeting fairness across several attributes often overlook so-called "fairness gerrymandering", thereby ignoring disparities among intersectional subgroups (e.g., African-American women or Hispanic men). In this paper, we propose a distance covariance regularisation framework that mitigates the association between model predictions and protected attributes, in line with the fairness definition of demographic parity, and that captures both linear and nonlinear dependencies. To enhance applicability in the presence of multiple protected attributes, we extend our framework by incorporating two multivariate dependence measures based on distance covariance: the previously proposed joint distance covariance (JdCov) and our novel concatenated distance covariance (CCdCov), which effectively address fairness gerrymandering in both regression and classification tasks involving protected attributes of various types. We discuss and illustrate how to calibrate regularisation strength, including a method based on Jensen-Shannon divergence, which quantifies dissimilarities in prediction distributions across groups. We apply our framework to the COMPAS recidivism dataset and a large motor insurance claims dataset.

The rise of misinformation underscores the need for scalable and reliable fact-checking solutions. Large language models (LLMs) hold promise in automating fact verification, yet their effectiveness across global contexts remains uncertain. We systematically evaluate nine established LLMs across multiple categories (open/closed-source, multiple sizes, diverse architectures, reasoning-based) using 5,000 claims previously assessed by 174 professional fact-checking organizations across 47 languages. Our methodology tests model generalizability on claims postdating training cutoffs and four prompting strategies mirroring both citizen and professional fact-checker interactions, with over 240,000 human annotations as ground truth. Findings reveal a concerning pattern resembling the Dunning-Kruger effect: smaller, accessible models show high confidence despite lower accuracy, while larger models demonstrate higher accuracy but lower confidence. This risks systemic bias in information verification, as resource-constrained organizations typically use smaller models. Performance gaps are most pronounced for non-English languages and claims originating from the Global South, threatening to widen existing information inequalities. These results establish a multilingual benchmark for future research and provide an evidence base for policy aimed at ensuring equitable access to trustworthy, AI-assisted fact-checking.

This paper presents the methodologies and results of the NOWJ team's participation across all five tasks at the COLIEE 2025 competition, emphasizing advancements in the Legal Case Entailment task (Task 2). Our comprehensive approach systematically integrates pre-ranking models (BM25, BERT, monoT5), embedding-based semantic representations (BGE-m3, LLM2Vec), and advanced Large Language Models (Qwen-2, QwQ-32B, DeepSeek-V3) for summarization, relevance scoring, and contextual re-ranking. Specifically, in Task 2, our two-stage retrieval system combined lexical-semantic filtering with contextualized LLM analysis, achieving first place with an F1 score of 0.3195. Additionally, in other tasks--including Legal Case Retrieval, Statute Law Retrieval, Legal Textual Entailment, and Legal Judgment Prediction--we demonstrated robust performance through carefully engineered ensembles and effective prompt-based reasoning strategies. Our findings highlight the potential of hybrid models integrating traditional IR techniques with contemporary generative models, providing a valuable reference for future advancements in legal information processing.

The release of open-weight large language models (LLMs) creates a tension between advancing accessible research and preventing misuse, such as malicious fine-tuning to elicit harmful content. Current safety measures struggle to preserve the general capabilities of the LLM while resisting a determined adversary with full access to the model's weights and architecture, who can use full-parameter fine-tuning to erase existing safeguards. To address this, we introduce AntiDote, a bi-level optimization procedure for training LLMs to be resistant to such tampering. AntiDote involves an auxiliary adversary hypernetwork that learns to generate malicious Low-Rank Adaptation (LoRA) weights conditioned on the defender model's internal activations. The defender LLM is then trained with an objective to nullify the effect of these adversarial weight additions, forcing it to maintain its safety alignment. We validate this approach against a diverse suite of 52 red-teaming attacks, including jailbreak prompting, latent space manipulation, and direct weight-space attacks. AntiDote is upto 27.4\% more robust against adversarial attacks compared to both tamper-resistance and unlearning baselines. Crucially, this robustness is achieved with a minimal trade-off in utility, incurring a performance degradation of upto less than 0.5\% across capability benchmarks including MMLU, HellaSwag, and GSM8K. Our work offers a practical and compute efficient methodology for building open-weight models where safety is a more integral and resilient property.

Retrieval-Augmented Generation (RAG) has transformed how we approach text generation tasks by grounding Large Language Model (LLM) outputs in retrieved knowledge. This capability is especially critical in the legal domain. In this work, we introduce a novel end-to-end RAG pipeline that improves upon previous baselines using three targeted enhancements: (i) a context-aware query translator that disentangles document references from natural-language questions and adapts retrieval depth and response style based on expertise and specificity, (ii) open-source retrieval strategies using SBERT and GTE embeddings that achieve substantial performance gains while remaining cost-efficient, and (iii) a comprehensive evaluation and generation framework that combines RAGAS, BERTScore-F1, and ROUGE-Recall to assess semantic alignment and faithfulness across models and prompt designs. Our results show that carefully designed open-source pipelines can rival proprietary approaches in retrieval quality, while a custom legal-grounded prompt consistently produces more faithful and contextually relevant answers than baseline prompting. Taken together, these contributions demonstrate the potential of task-aware, component-level tuning to deliver legally grounded, reproducible, and cost-effective RAG systems for legal research assistance.