Lavish new Broadway show closes prematurely... after its A-List star hailed Charlie Kirk in wake of his assassination Tourists warned against visiting 8 popular destinations in 2026 - including European hotspot where locals don't want you I know why Usha Vance ditched her wedding ring. Most women would do the same if they'd suffered her humiliation: KENNEDY Troubled 350lb son of Hollywood icon is forced to humiliating new low... as his movie star brother luxuriates in $7m Montecito mansion Alex appeared to have the dream Manhattan mom life. But she was hiding a dark secret... and it almost killed her Dick's Sporting Goods announces closures with hundreds of Foot Locker stores at risk Billionaire family posts VERY unusual obituary after heir, 40, met violent end at $2.8m hunting lodge following marriage scandal I drink water constantly but have terrible dry mouth. DR KAYE reveals the simple solution to get rid of the problem for good... and the serious immune condition it could be hiding Anna Wintour breaks her silence on Jeff Bezos and Lauren Sanchez sponsoring next year's Met Gala Fury as'biological male' wins World's Strongest Woman event in Texas... as rivals say they had no idea of athlete's background Alleged sex abuse, gay slurs and that'stoned' family dinner: Full details of the explosive feud tearing Richard Dreyfuss and his children apart Visa-dodger from Finland finally gets her just desserts as ICE boots her from American after shocking attack on BABY, mom, and dog'Ponce' World's coolest streets revealed - as two UK high streets make the top 31 Trump's losing control... MAGA's imploding... and White House insiders tell me why they're REALLY worried: ANDREW NEIL Meghan Markle slammed for risky kitchen blunder while promoting As Ever: 'No one lives like this' A new study has shed light on incels, and exactly why some men are more likely to become women-hating recluses. Psychologists in Spain conducted a review of scientific studies about incels - the growing online subculture of'involuntarily celibates'.

EU and US unshackle regulations in quest for growth, and is the AI bubble about to burst? In tech, the European Union is deregulating artificial intelligence; the United States is going even further. The AI bubble has not popped, thanks to Nvidia's astronomical quarterly earnings, but fears persist. And Meta has avoided a breakup for a similar reason as Google. The hundreds of billions of dollars being spent on AI are overwhelming Europe's commitment to digital privacy and stringent tech regulation.

YouTuber "Schlep built a huge following tracking down alleged child predators on Roblox before being kicked off. The platform is facing multiple lawsuits over child safety. Last month, Kentucky attorney general Russell Coleman announced the details of yet another lawsuit against Roblox over suspected pedophiles lurking on the hugely popular gaming platform. While doing so, Coleman singled out the work of one self-described "predator hunter" who claims to have helped identify alleged abusers mixing with young gamers. "Roblox is even trying to silence those who raised these security risks," Coleman said. "The famous case of one of their developers, Schlep, immediately comes to mind." Schlep is in fact Michael, a 22-year-old Texan who has spent the last two years working with a group of other Roblox players to track down and identify people purportedly seeking to groom young children on the platform--predators like the one Schlep says allegedly groomed him a decade ago, which he says led ...

Two years after he was found guilty of fraud, FTX founder Sam Bankman-Fried is pursuing a legal appeal--and firing up his X account. On September 23, for the first time in more than six months, an X account belonging to disgraced FTX founder Sam Bankman-Fried published a post . It simply read, "gm"--internet slang for "good morning." The account has been posting consistently since. Bankman-Fried--known widely as SBF--is currently serving a 25-year prison sentence in California.

As AI systems evolve from static tools to dynamic agents, traditional categorical governance frameworks -- based on fixed risk tiers, levels of autonomy, or human oversight models -- are increasingly insufficient on their own. Systems built on foundation models, self-supervised learning, and multi-agent architectures increasingly blur the boundaries that categories were designed to police. In this Perspective, we make the case for dimensional governance: a framework that tracks how decision authority, process autonomy, and accountability (the 3As) distribute dynamically across human-AI relationships. A critical advantage of this approach is its ability to explicitly monitor system movement toward and across key governance thresholds, enabling preemptive adjustments before risks materialize. This dimensional approach provides the necessary foundation for more adaptive categorization, enabling thresholds and classifications that can evolve with emerging capabilities. While categories remain essential for decision-making, building them upon dimensional foundations allows for context-specific adaptability and stakeholder-responsive governance that static approaches cannot achieve. We outline key dimensions, critical trust thresholds, and practical examples illustrating where rigid categorical frameworks fail -- and where a dimensional mindset could offer a more resilient and future-proof path forward for both governance and innovation at the frontier of artificial intelligence.

Data Flows and Colonial Regimes in Africa: A Critical Analysis of the Colonial Futurities Embedded in AI Recommendation Algorithms Angella Ndaka, University of Witwatersrand, Johannesburg, South Africa Fátima Ávila - Acosta, Berlin Graduate School of Social Sciences at Humboldt University, Berlin, Germany Harnred Mbula, Centre for Epistemic Justice, Nairobi, Kenya Christine Amera, Centre for Epistemic Justice, Nairobi Kenya Sandra Tiyani Chauke University of Pretoria, South Africa Eucabeth Majiwa Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya Abstract In the last few years, Africa has experienced growth in a thriving ecosystem of Artificial Intelligence (AI) technologies and systems, developed and promoted by both local and global technology players. While the sociotechnical imaginaries about these syst ems promote AI as critical to achiev ing Africa's sustainable development agenda, some of them have subtly permeated society, recreating new values, cultures, practices, and histories that threaten to marginalize minority groups in the region. Africa predominantly frames AI as an imaginary solution to address complex social challenges; however, the narrative subtly ignores deeper power - related concerns, including data governance, embedded algorithmic colonialism, and the exploitation that propag ates new digital colonial sites. However, the development of current AI ethics in Africa is in its infancy and predominantly framed through lenses of Western perspective, with the social and ethical impacts of the AI innovations and application on African epistemologies and worldviews not prioritized. To ensure that people on the African continent leverage the benefits of AI, these social and ethical impacts o f AI need to be critically and explicitly considered and addressed. This chapter will therefore seek to frame the elemental and invisible problems of AI and big data in the African context by examining digital sites and infrastructure through the lens of power and interests. It will present reflections on how these sites are using AI recommendation algorithms to recreate new digital societies in the region, how they have the potential to propagate algorithmic colonialism and negative gender norms, and what this means for the regional sustainable development agenda. The chapter proposes adopting business models that embrace response - ability and consider the existence of alternative socio - material worlds of AI. These reflections will mainly come from ongoing discussions with Kenyan social media users in this author's user space talks, which take place every month. Keywords: Artificial Intelligence; algorithmic colonialism; Data; response - ability; digital sites Section 1: Introduction The growing global interest, combined with rising investments in AI skilling and infrastructure development, is a key driver of the expanding landscape of AI technologies and systems across Africa.

The advent of financial technology has witnessed a surge in the utilization of deep learning models to anticipate consumer conduct, a trend that has demonstrated considerable potential in enhancing lending strategies and bolstering market efficiency. We study multi-horizon demand forecasting on e-commerce transactions using the UCI Online Retail II dataset. Unlike prior versions of this manuscript that mixed financial-loan narratives with retail data, we focus exclusively on retail market behavior and define a clear prediction target: per SKU daily demand (or revenue) for horizons H=1,7,14. We present a hybrid sequence model that combines multi-scale temporal convolutions, a gated recurrent module, and time-aware self-attention. The model is trained with standard regression losses and evaluated under MAE, RMSE, sMAPE, MASE, and Theil's U_2 with strict time-based splits to prevent leakage. We benchmark against ARIMA/Prophet, LSTM/GRU, LightGBM, and state-of-the-art Transformer forecasters (TFT, Informer, Autoformer, N-BEATS). Results show consistent accuracy gains and improved robustness on peak/holiday periods. We further provide ablations and statistical significance tests to ensure the reliability of improvements, and we release implementation details to facilitate reproducibility.

Large language models demonstrate powerful capabilities across various natural language processing tasks, yet they also harbor safety vulnerabilities. To enhance LLM safety, various jailbreak defense methods have been proposed to guard against harmful outputs. However, improvements in model safety often come at the cost of severe over-refusal, failing to strike a good balance between safety and usability. In this paper, we first analyze the causes of over-refusal from a representation perspective, revealing that over-refusal samples reside at the boundary between benign and malicious samples. Based on this, we propose MOSR, designed to mitigate over-refusal by intervening the safety representation of LLMs. MOSR incorporates two novel components: (1) Overlap-Aware Loss Weighting, which determines the erasure weight for malicious samples by quantifying their similarity to pseudo-malicious samples in the representation space, and (2) Context-Aware Augmentation, which supplements the necessary context for rejection decisions by adding harmful prefixes before rejection responses. Experiments demonstrate that our method outperforms existing approaches in mitigating over-refusal while largely maintaining safety. Overall, we advocate that future defense methods should strike a better balance between safety and over-refusal.

Content moderation filters are a critical safeguard against alignment failures in language models. Yet most existing filters focus narrowly on general safety and overlook cultural context. In this work, we introduce FanarGuard, a bilingual moderation filter that evaluates both safety and cultural alignment in Arabic and English. We construct a dataset of over 468K prompt and response pairs, drawn from synthetic and public datasets, scored by a panel of LLM judges on harmlessness and cultural awareness, and use it to train two filter variants. To rigorously evaluate cultural alignment, we further develop the first benchmark targeting Arabic cultural contexts, comprising over 1k norm-sensitive prompts with LLM-generated responses annotated by human raters. Results show that FanarGuard achieves stronger agreement with human annotations than inter-annotator reliability, while matching the performance of state-of-the-art filters on safety benchmarks. These findings highlight the importance of integrating cultural awareness into moderation and establish FanarGuard as a practical step toward more context-sensitive safeguards.

Large language models are evolving from single-turn responders into tool-using agents capable of sustained reasoning and decision-making for deep research. Prevailing systems adopt a linear pipeline of plan to search to write to a report, which suffers from error accumulation and context rot due to the lack of explicit control over both model behavior and context. We introduce RhinoInsight, a deep research framework that adds two control mechanisms to enhance robustness, traceability, and overall quality without parameter updates. First, a Verifiable Checklist module transforms user requirements into traceable and verifiable sub-goals, incorporates human or LLM critics for refinement, and compiles a hierarchical outline to anchor subsequent actions and prevent non-executable planning. Second, an Evidence Audit module structures search content, iteratively updates the outline, and prunes noisy context, while a critic ranks and binds high-quality evidence to drafted content to ensure verifiability and reduce hallucinations. Our experiments demonstrate that RhinoInsight achieves state-of-the-art performance on deep research tasks while remaining competitive on deep search tasks.