America's richest real estate tycoon disowns son with shockingly icy 12-word statement after'man cave' plans went terribly wrong Horrific stab wounds suffered by grease truck driver, 69, 'stabbed by Mark Sanchez' with NFL star facing up to six years in prison Taylor Swift makes surprise confession on her song'about ex Joe Alwyn' as she insists fans have'always had the wrong idea' about it Sinister notes that are plaguing remote county explodes as fears mount over creepy messages: 'What else could they do?' Key North Atlantic current is on the brink of COLLAPSING - plunging Europe into a'Little Ice Age', scientists warn Visionary billionaire died in a suspicious house fire. Then a mysterious will emerged... CBS staff in panic as anti-woke firebrand Bari Weiss takes control with no-nonsense show on America's most divisive issues Trump's war room plots savage bloodbath as countdown enters final hours: Live updates Trump sends Navy officers wild with powerful message to liberals claiming he's'unwell' We got hopelessly hooked on a trendy'wellness' tonic. We thought it was harmless but our descent into addiction left us depressed, in debt... and in rehab Judge speaks out after her $1.5m mansion'exploded' in suspected arson attack after she defied Trump order Mark Sanchez's alleged victim's family breaks silence as grim photos emerge after violent attack So many women suffer bloated, uncomfortable guts, says DR EMILY LEEMING. Here's the 7 simple cures I give my patients - you won't have read these before My son made a horrifying accusation about me in therapy... it's destroyed our relationship: DEAR JANE Ex-NFL star Mark Sanchez'thought he'd been shot and pounded on window of pub to get help', bartender reveals Nicole Kidman's friends tear into Keith Urban over bombshell split: 'Total 180 on who he is' Real Housewives of Atlanta vet Porsha Williams reveals she is dating a woman... after ex Simon was deported by ICE US billionaire retail estate tycoon is ordered to sell off his'exceptional' £36million London mansion in bitter divorce battle with ex-wife My husband works in Dubai and has cheated on me at least three times so far.

Booth is a reporter at TIME. Booth is a reporter at TIME. Julian Pintat, a freelance English-to-German translator has watched his 15-year career gradually unravel. Specializing in high-stakes fields like medical technology and pharmaceutics, his expertise has been repriced as an AI cleanup service. Fixing such basic flaws, which now constitutes 95% of his work, often takes longer than translating from scratch, he says--a frustrating reality that has halved his income and put life plans including marriage and starting a family on indefinite hold.

Meta's leaked internal document reveals AI chatbot guidelines that strictly forbid sexual roleplay with minors amid FTC investigation into child safety.

At an adult industry conference in Prague last month, delegates noted a sharp increase in sites offering users the chance to form AI relationships. At an adult industry conference in Prague last month, delegates noted a sharp increase in sites offering users the chance to form AI relationships. 'Obedient, yielding and happy to follow': the troubling rise of AI girlfriends E leanor, 24, is a Polish historian and lecturer at a university in Warsaw; Isabelle, 25, is a detective serving with the NYPD; Brooke, 39, is an American housewife who enjoys an opulent Miami lifestyle financed by her frequently absent husband. All three women will flirt and chat and send nude photographs and explicit videos via one of a soaring number of new adult dating websites that offer an increasingly realistic selection of AI girlfriends for subscribers willing to pay a monthly fee. At the TES adult industry conference in Prague last month, delegates noted a sharp increase in new websites offering users the chance to form relationships with AI-generated girlfriends, who will remove their clothes in exchange for tokens purchased by bank transfer.

OpenAI's Sora 2 app allows users to make AI-generated videos based on a text prompt. OpenAI's Sora 2 app allows users to make AI-generated videos based on a text prompt. Company behind the AI video app says it will work with rights holders to'block characters from Sora at their request' Mon 6 Oct 2025 00.10 EDTLast modified on Mon 6 Oct 2025 00.11 EDT Sora 2, a video generator powered by artificial intelligence, was launched last week on an invite-only basis. The app allows users to generate short videos based on a text prompt. Varun Shetty, OpenAI's head of media partnerships, said: "We'll work with rights holders to block characters from Sora at their request and respond to takedown requests."

Recommender systems have traditionally followed modular architectures comprising candidate generation, multi-stage ranking, and re-ranking, each trained separately with supervised objectives and hand-engineered features. While effective in many domains, such systems face persistent challenges including sparse and noisy interaction data, cold-start problems, limited personalization depth, and inadequate semantic understanding of user and item content. The recent emergence of Large Language Models (LLMs) offers a new paradigm for addressing these limitations through unified, language-native mechanisms that can generalize across tasks, domains, and modalities. In this paper, we present a comprehensive technical survey of how LLMs can be leveraged to tackle key challenges in modern recommender systems. We examine the use of LLMs for prompt-driven candidate retrieval, language-native ranking, retrieval-augmented generation (RAG), and conversational recommendation, illustrating how these approaches enhance personalization, semantic alignment, and interpretability without requiring extensive task-specific supervision. LLMs further enable zero- and few-shot reasoning, allowing systems to operate effectively in cold-start and long-tail scenarios by leveraging external knowledge and contextual cues. We categorize these emerging LLM-driven architectures and analyze their effectiveness in mitigating core bottlenecks of conventional pipelines. In doing so, we provide a structured framework for understanding the design space of LLM-enhanced recommenders, and outline the trade-offs between accuracy, scalability, and real-time performance. Our goal is to demonstrate that LLMs are not merely auxiliary components but foundational enablers for building more adaptive, semantically rich, and user-centric recommender systems

Audio Language Models (ALMs) have made significant progress recently. These models integrate the audio modality directly into the model, rather than converting speech into text and inputting text to Large Language Models (LLMs). While jailbreak attacks on LLMs have been extensively studied, the security of ALMs with audio modalities remains largely unexplored. Currently, there is a lack of an adversarial audio dataset and a unified framework specifically designed to evaluate and compare attacks and ALMs. In this paper, we present JALMBench, a comprehensive benchmark to assess the safety of ALMs against jailbreak attacks. JALMBench includes a dataset containing 11,316 text samples and 245,355 audio samples with over 1,000 hours. It supports 12 mainstream ALMs, 4 text-transferred and 4 audio-originated attack methods, and 5 defense methods. Using JALMBench, we provide an in-depth analysis of attack efficiency, topic sensitivity, voice diversity, and architecture. Additionally, we explore mitigation strategies for the attacks at both the prompt level and the response level.

The development of generative artificial intelligence (AI) tools capable of producing wholly or partially synthetic child sexual abuse material (AI CSAM) presents profound challenges for child protection, law enforcement, and societal responses to child exploitation. While some argue that the harmfulness of AI CSAM differs fundamentally from other CSAM due to a perceived absence of direct victimization, this perspective fails to account for the range of risks associated with its production and consumption. AI has been implicated in the creation of synthetic CSAM of children who have not previously been abused, the revictimization of known survivors of abuse, the facilitation of grooming, coercion and sexual extortion, and the normalization of child sexual exploitation. Additionally, AI CSAM may serve as a new or enhanced pathway into offending by lowering barriers to engagement, desensitizing users to progressively extreme content, and undermining protective factors for individuals with a sexual interest in children. This paper provides a primer on some key technologies, critically examines the harms associated with AI CSAM, and cautions against claims that it may function as a harm reduction tool, emphasizing how some appeals to harmlessness obscure its real risks and may contribute to inertia in ecosystem responses.

Large Language Models (LLMs) are increasingly fine-tuned on smaller, domain-specific datasets to improve downstream performance. Existing membership inference attacks (MIAs) and dataset-inference methods typically require access to internal signals such as log-its, while current black-box approaches often rely on handcrafted prompts or a clean reference dataset for calibration, both of which limit practical applicability. Watermarking is a promising alternative, but prior techniques can degrade text quality or reduce task performance. TRACE rewrites datasets with distortion-free watermarks guided by a private key, ensuring both text quality and downstream utility. At detection time, we exploit the radioactivity effect of fine-tuning on watermarked data and introduce an entropy-gated procedure that selectively scores high-uncertainty tokens, substantially amplifying detection power. Across diverse datasets and model families, TRACE consistently achieves significant detections (p < 0.05), often with extremely strong statistical evidence. Furthermore, it supports multi-dataset attribution and remains robust even after continued pretraining on large non-watermarked corpora. Large Language Models (LLMs) have demonstrated strong performance across real-world applications, from conversational agents (Thoppilan et al. (2022)) and educational tutoring (Wang et al. (2024)) to medical support (Thirunavukarasu et al. (2023)). Their capabilities stem from pre-training on massive text corpora (Hoffmann et al. (2022)) and, crucially for real deployments, from subsequent fine-tuning on smaller, domain-specific datasets curated by enterprises or individual researchers (Wei et al. (2021)).

Multi-hop reasoning over financial disclosures is often a retrieval problem before it becomes a reasoning or generation problem: relevant facts are dispersed across sections, filings, companies, and years, and LLMs often expend excessive tokens navigating noisy context. Without precise Knowledge Graph (KG)-guided selection of relevant context, even strong reasoning models either fail to answer or consume excessive tokens, whereas KG-linked evidence enables models to focus their reasoning on composing already retrieved facts. We present FinReflectKG - MultiHop, a benchmark built on FinReflectKG, a temporally indexed financial KG that links audited triples to source chunks from S&P 100 filings (2022-2024). Mining frequent 2-3 hop subgraph patterns across sectors (via GICS taxonomy), we generate financial analyst style questions with exact supporting evidence from the KG. A two-phase pipeline first creates QA pairs via pattern-specific prompts, followed by a multi-criteria quality control evaluation to ensure QA validity. We then evaluate three controlled retrieval scenarios: (S1) precise KG-linked paths; (S2) text-only page windows centered on relevant text spans; and (S3) relevant page windows with randomizations and distractors. Across both reasoning and non-reasoning models, KG-guided precise retrieval yields substantial gains on the FinReflectKG - MultiHop QA benchmark dataset, boosting correctness scores by approximately 24 percent while reducing token utilization by approximately 84.5 percent compared to the page window setting, which reflects the traditional vector retrieval paradigm. Spanning intra-document, inter-year, and cross-company scopes, our work underscores the pivotal role of knowledge graphs in efficiently connecting evidence for multi-hop financial QA. We also release a curated subset of the benchmark (555 QA Pairs) to catalyze further research.