Senator accused of steamy affair with her bodyguard in bombshell lawsuit from his WIFE: 'Bring MDMA so I can guide you' Socialite who accused playboy twins of sex attack at Hamptons'castle' is found dead in unexplained circumstances Amy Schumer's friends reveal true meaning of thin bikini pictures and why they're'monitoring her'... as depth of ex Chris Fischer's heartbreak is laid bare Hunter Biden's stripper baby mama asks for him to be ARRESTED over claims he is still failing to pay her child support Ellen Greenberg's fiancé Sam Goldberg breaks cover as feds reopen probe into her'suicide'... and late teacher's mother shares incredible sign sent from beyond the grave Nicole Richie addresses her daughter's new identity after unveiling transformation on her 18th birthday '90s Vogue model Niki Taylor looks amazing as she sizzles at age 50 for new campaign Karoline Leavitt reveals the thinking behind Trump's call to cancel elections Family of Tyler Robinson's transgender lover speaks ...

Gunfire and anti-aircraft blasts heard near Venezuela's presidential palace as chaos unfolds after Maduro's ouster Stephen Miller declares Greenland should be part of US and'nobody will fight' over country's future Timothee Chalamet's declaration of love to his pneumatic girlfriend Kylie Jenner at the Critics Choice Awards has left me with a terribly rude thought: CAROLINE BULLOCK Trump vowed to deport one million migrants. But insiders say explosive data that Kristi Noem is desperate to hide tells the REAL story... The View audience left stunned as woke anti-MAGA co-host defends Trump's arrest of Maduro Trump says US will have to pay oil companies to rebuild Venezuela's aging infrastructure as he declares HIMSELF in charge of ambitious 18-month plan Furious fallen dictator Nicolás Maduro's hearing descends into chaos as he gets into shouting match with man claiming he was prisoner of his regime JFK's grandson Jack Schlossberg, 32, looks heartbroken as he attends sister Tatiana's funeral after she died of cancer aged just 35, with Joe Biden seen crying Kylie Jenner's curves spark surgery rumors as Timothee Chalamet grabs her behind at Critics Choice Awards SNL star Chloe Fineman reveals'botched' cosmetic treatments in candid photos as fans beg her to stop Secrets of JD Vance's'home attacker': Suspect is transgender daughter of wealthy surgeon Democrat donor as ultra-privileged life is revealed Ancient Bible reveals timeline for humanity's final'day' before divine judgment Gmail users advised to'turn off' two features NOW amid email privacy concerns'Super flu' still spreading uncontrollably... as cities see record number of cases and hospitalizations My shock discovery made me rethink everything I know about death. This is exactly what happens after your heart stops beating... and what you'll see, reveals neurosurgeon Caroline Kennedy cradles granddaughter at her daughter Tatiana Schlossberg's funeral, as doctor widower holds onto their son Gmail users advised to'turn off' two features NOW amid email privacy concerns Google users have been warned that they've been secretly opted in to a feature that allows the tech giant to access all their private emails. According to electronics design engineer Dave Jones of Australia, all Gmail users have had their accounts automatically selected to allow Google to scan their messages and attachments to help train its AI models like Gemini.

Modern large language models (LLMs) demonstrate exceptional performance on knowledge-intensive tasks, yet the theoretical mechanisms underlying knowledge acquisition (storage and memorization) during pre-training and extraction (retrieval and recall) during inference after fine-tuning remain poorly understood. Although prior theoretical studies have explored these processes through analyses of training dynamics, they overlook critical components essential for a comprehensive theory: (1) the multi-layer perceptron (MLP), empirically identified as the primary module for knowledge storage; (2) out-of-distribution (OOD) adaptivity, which enables LLMs to generalize to unseen scenarios post-pre-training; and (3) next-token prediction, the standard autoregressive objective that encodes knowledge as conditional probabilities. In this work, we introduce, to the best of our knowledge, the first theoretical framework that addresses these limitations by examining the training dynamics of one-layer transformers. Under regularity assumptions, we establish that: (i) transformers attain near-optimal training loss during pre-training, demonstrating effective knowledge acquisition; (ii) given a sufficiently large fine-tuning dataset and appropriate data multiplicity conditions, transformers achieve low generalization error on factual knowledge acquired during pre-training but not revisited in fine-tuning, indicating robust knowledge extraction; and (iii) violation of these conditions leads to elevated generalization error, manifesting as hallucinations. Our analysis encompasses both full fine-tuning and low-rank fine-tuning, yielding insights into the efficacy of practical low-rank adaptation methods. We validate our theoretical findings through experiments on synthetic datasets and the real-world PopQA benchmark, employing GPT-2 and Llama-3.2-1B models.

Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, we introduce FinSight (Financial InSight), a novel multi agent framework for producing high-quality, multimodal financial reports. The foundation of FinSight is the Code Agent with Variable Memory (CAVM) architecture, which unifies external data, designed tools, and agents into a programmable variable space, enabling flexible data collection, analysis and report generation through executable code. To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism that progressively refines raw visual outputs into polished financial charts. Furthermore, a two stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports, ensuring both analytical depth and structural consistency. Experiments on various company and industry-level tasks demonstrate that FinSight significantly outperforms all baselines, including leading deep research systems in terms of factual accuracy, analytical depth, and presentation quality, demonstrating a clear path toward generating reports that approach human-expert quality.

Large Language Models (LLMs) are rapidly being adopted in conservation to address the biodiversity crisis, yet their reliability for species evaluation is uncertain. This study systematically validates five leading models on 21,955 species across four core IUCN Red List assessment components: taxonomy, conservation status, distribution, and threats. A critical paradox was revealed: models excelled at taxonomic classification (94.9%) but consistently failed at conservation reasoning (27.2% for status assessment). This knowledge-reasoning gap, evident across all models, suggests inherent architectural constraints, not just data limitations. Furthermore, models exhibited systematic biases favoring charismatic vertebrates, potentially amplifying existing conservation inequities. These findings delineate clear boundaries for responsible LLM deployment: they are powerful tools for information retrieval but require human oversight for judgment-based decisions. A hybrid approach is recommended, where LLMs augment expert capacity while human experts retain sole authority over risk assessment and policy.

Machine unlearning is concerned with the task of removing knowledge learned from particular data points from a trained model. In the context of large language models (LLMs), unlearning has recently received increased attention, particularly for removing knowledge about named entities from models for privacy purposes. While various approaches have been proposed to address the unlearning problem, most existing approaches treat all data points to be unlearned equally, i.e., unlearning that Montreal is a city in Canada is treated exactly the same as unlearning the phone number of the first author of this paper. In this work, we show that this all data is equal assumption does not hold for LLM unlearning. We study how the success of unlearning depends on the frequency of the knowledge we want to unlearn in the pre-training data of a model and find that frequency strongly affects unlearning, i.e., more frequent knowledge is harder to unlearn. Additionally, we uncover a misalignment between probability and generation-based evaluations of unlearning and show that this problem worsens as models become larger. Overall, our experiments highlight the need for better evaluation practices and novel methods for LLM unlearning that take the training data of models into account.

Conventional anti-money laundering (AML) systems predominantly focus on identifying anomalous entities or transactions, flagging them for manual investigation based on statistical deviation or suspicious behavior. This paradigm, however, misconstrues the true nature of money laundering, which is rarely anomalous but often deliberate, repeated, and concealed within consistent behavioral routines. In this paper, we challenge the entity-centric approach and propose a network-theoretic perspective that emphasizes detecting predefined laundering patterns across directed transaction networks. We introduce the notion of behavioral consistency as the core trait of laundering activity, and argue that such patterns are better captured through subgraph structures expressing semantic and functional roles - not solely geometry. Crucially, we explore the concept of pattern fragility: the sensitivity of laundering patterns to small attribute changes and, conversely, their semantic robustness even under drastic topological transformations. We claim that laundering detection should not hinge on statistical outliers, but on preservation of behavioral essence, and propose a reconceptualization of pattern similarity grounded in this insight. This philosophical and practical shift has implications for how AML systems model, scan, and interpret networks in the fight against financial crime.

The boss of the AI tool ChatGPT has revealed that his eyeball-scanning orbs are coming to the US, as questions still swirl around this dystopian step into the future. Sam Altman announced Wednesday that the identity verification technology will now be available in six cities - Atlanta, Austin, Los Angeles, Miami, Nashville, and San Francisco. The expansion into the US is all part of Altman's plan to create a new global identity and financial network. Currently, Altman's cryptocurrency company World has rolled out the orb devices in more than 35 cities across over 20 countries worldwide. The main purpose of these eyeball scanners is to verify that each user is a'unique human,' not a bot or duplicate account.

Haiti police raid gang leader's stronghold in capital 3 hours agoShareSaveLeonardo RochaBBC World Service Americas regional editor Jaroslav LukivBBC NewsShareSaveReutersGang control in Port-au-Prince has led to an almost complete breakdown of law and order The government of Haiti says police have launched a large-scale operation in a shantytown controlled by powerful gang leader Jimmy Chérizier, who is widely known as Barbecue. The authorities say several gang members have been killed in the Lower Delmas area of the capital Port-au-Prince. Local reports say military drones carrying explosives are being used in the operation. He said it was the work of a special task force created two days ago to tackle insecurity.Reuters Jimmy'Barbecue' Chérizier has become one of the most powerful gang leaders in Haiti Chérizier, aged 47, is the feared leader of Viv Ansam (Live Together), a coalition of gangs that control much of the city. It is not clear whether Kenyan police officers deployed in Haiti last year to help fight the gangs are involved in the security operation.

Understanding the effects of economic shocks on firms is critical for analyzing economic growth and resilience. We introduce a Web-Based Affectedness Indicator (W AI), a general-purpose tool for real-time monitoring of economic disruptions across diverse contexts. By leveraging Large Language Model (LLM) assisted classification and information extraction on texts from over five million company websites, W AI quantifies the degree and nature of firms' responses to external shocks. Using the COVID-19 pandemic as a specific application, we show that W AI is highly correlated with pandemic containment measures and reliably predicts firm performance. Unlike traditional data sources, W AI provides timely firm-level information across industries and geographies worldwide that would otherwise be unavailable due to institutional and data availability constraints. This methodology offers significant potential for monitoring and mitigating the impact of technological, political, financial, health or environmental crises, and represents a transformative tool for adaptive policy-making and economic resilience. Economic shocks, whether driven by public health crises, technological disruptions, geopolitical conflicts, or climate events, pose significant challenges to businesses and policymakers alike. Timely and accurate monitoring of these shocks is critical for crafting effective responses and enhancing economic resilience. However, traditional methods for measuring the impacts of such disruptions - such as surveys and administrative data - are often limited by costs, time lags, and coverage. In this study, we introduce the Web-Based Affectedness Indicator (W AI), a scalable and cost-effective tool for real-time monitoring of economic disruptions at the firm level. By analyzing textual data from millions of company websites, W AI provides granular insights into how firms experience and respond to external shocks. This 1 methodology overcomes traditional limitations by leveraging ubiquitous online content and state-of-the-art natural language processing (NLP) models to generate a dynamic and comprehensive view of economic affectedness. W AI can provide information on a wide range of challenges, including supply chain disruptions, financial crises, and climate-related shocks.