Ben Affleck and Jennifer Garner's daughter Violet emotionally advocates for mask mandates and children with long COVID at United Nations event Jimmy Kimmel weeps while saying he'never intended' to'make light of' Charlie Kirk's death - but DOESN'T apologize as he hits out at Trump If Trump isn't careful, he will end up no better than Biden! This dirty revenge tour must cease... before everyone loses: DAN MCLAUGHLIN Jimmy Kimmel's comeback descends into chaos: Staff turn on host over'sh***y' behavior... as'betrayal rumor' runs rife backstage Charlie Kirk suspect's trans lover has VANISHED: Shaken neighbors share fresh fears... as new photos show abandoned home Jimmy Kimmel's return BLASTED by Roseanne Barr seven years after ABC fired her: 'Double standard' I'm the doctor on the cusp of an autism breakthrough... we're using an everyday $2.50 pill to reverse children's symptoms Dancing with the Stars drama explodes: Cast are'miserable'... concerned family say smiles on screen are FAKE... and producers are forced to issue'warning' The world's best burgers REVEALED - and London bags nearly half of the top ten spots (but number one will surprise you) I was a devout Catholic... until I died. Moment daughter of Trump's would-be assassin Ryan Routh LOSES IT outside of court after father convicted of trying to kill president Sarah Ferguson claims she was trying to protect Princesses Beatrice and Eugenie when she sent apology email to Jeffrey Epstein'as her children come first' The View co-host makes cheeky immigration crack about Kamala Harris' Miami book tour stop SARAH VINE: The striking similarities between Sarah Ferguson and Meghan... and why Fergie's downfall should be a red flag for the Sussexes Chappell Roan'accidentally' reveals derrière onstage: 'I forgot my bottom was just a thong' Kim Kardashian takes a pop at Kanye as she poses topless for Vogue: 'I gained confidence three years ago... before, I always needed to check with someone before making decisions' Were you convinced by the Rapture? You're probably ARROGANT: People who believe in conspiracy theories are'massively overconfident', study finds READ MORE: Devout Christians take drastic action as'The Rapture' approaches Thousands of people around the world woke up yesterday morning hoping it would be their last day on Earth. The'Rapture' was a theory put forward by a South African pastor, claiming that Jesus would return to Earth on September 23, causing his followers to rise into the sky to meet him.

Things to Do in L.A. She thought she talking to her favorite celebrity. This is read by an automated voice. Please report any issues or inconsistencies here . Abigail Ruvalcaba was intrigued when a handsome daytime soap opera actor she'd been watching for years reached out to her in a Facebook message. His rugged exterior softened by his piercing blue eyes and an almost shy smile disarmed her.

It's surprisingly easy to stumble into a relationship with an AI chatbot Looking for help with her art project, she strikes up a conversation with her assistant. One thing leads to another, and suddenly she has a boyfriend she's introducing to her friends and family. Her new companion is an AI chatbot. The first large-scale computational analysis of the Reddit community r/MyBoyfriendIsAI, an adults-only group with more than 27,000 members, has found that this type of scenario is now surprisingly common. In fact, many of the people in the subreddit, which is dedicated to discussing AI relationships, formed those relationships unintentionally while using AI for other purposes. Researchers from MIT found that members of this community are more likely to be in a relationship with general-purpose chatbots like ChatGPT than companionship-specific chatbots such as Replika.

Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning, positioning them as promising tools for supporting human problem-solving. However, what happens when their performance is affected by misinformation, i.e., incorrect inputs introduced by users due to oversights or gaps in knowledge? Such misinformation is prevalent in real-world interactions with LLMs, yet how it propagates within LLMs' reasoning process remains underexplored. Focusing on mathematical reasoning, we present a comprehensive analysis of how misinformation affects intermediate reasoning steps and final answers. We also examine how effectively LLMs can correct misinformation when explicitly instructed to do so. Even with explicit instructions, LLMs succeed less than half the time in rectifying misinformation, despite possessing correct internal knowledge, leading to significant accuracy drops (10.02% - 72.20%), and the degradation holds with thinking models (4.30% - 19.97%). Further analysis shows that applying factual corrections early in the reasoning process most effectively reduces misinformation propagation, and fine-tuning on synthesized data with early-stage corrections significantly improves reasoning factuality. Our work offers a practical approach to mitigating misinformation propagation.

People often ask questions with false assumptions, a type of question that does not have regular answers. Answering such questions requires first identifying the false assumptions. Large Language Models (LLMs) often generate misleading answers to these questions because of hallucinations. In this paper, we focus on identifying and answering questions with false assumptions in several domains. We first investigate whether the problem reduces to fact verification. Then, we present an approach leveraging external evidence to mitigate hallucinations. Experiments with five LLMs demonstrate that (1) incorporating retrieved evidence is beneficial and (2) generating and validating atomic assumptions yields more improvements and provides an interpretable answer by pinpointing the false assumptions.

Generative propaganda is the use of generative artificial intelligence (AI) to shape public opinion. To characterize its use in real-world settings, we conducted interviews with defenders (e.g., factcheckers, journalists, officials) in Taiwan and creators (e.g., influencers, political consultants, advertisers) as well as defenders in India, centering two places characterized by high levels of online propaganda. The term "deepfakes", we find, exerts outsized discursive power in shaping defenders' expectations of misuse and, in turn, the interventions that are prioritized. To better characterize the space of generative propaganda, we develop a taxonomy that distinguishes between obvious versus hidden and promotional versus derogatory use. Deception was neither the main driver nor the main impact vector of AI's use; instead, Indian creators sought to persuade rather than to deceive, often making AI's use obvious in order to reduce legal and reputational risks, while Taiwan's defenders saw deception as a subset of broader efforts to distort the prevalence of strategic narratives online. AI was useful and used, however, in producing efficiency gains in communicating across languages and modes, and in evading human and algorithmic detection. Security researchers should reconsider threat models to clearly differentiate deepfakes from promotional and obvious uses, to complement and bolster the social factors that constrain misuse by internal actors, and to counter efficiency gains globally.

Disinformation is among the top risks of generative artificial intelligence (AI) misuse. Global adoption of generative AI necessitates red-teaming evaluations (i.e., systematic adversarial probing) that are robust across diverse languages and cultures, but red-teaming datasets are commonly US- and English-centric. To address this gap, we propose "anecdoctoring", a novel red-teaming approach that automatically generates adversarial prompts across languages and cultures. We collect misinformation claims from fact-checking websites in three languages (English, Spanish, and Hindi) and two geographies (US and India). We then cluster individual claims into broader narratives and characterize the resulting clusters with knowledge graphs, with which we augment an attacker LLM. Our method produces higher attack success rates and offers interpretability benefits relative to few-shot prompting. Results underscore the need for disinformation mitigations that scale globally and are grounded in real-world adversarial misuse.

Recent advances in diffusion models have significantly improved image and video synthesis. In addition, several concept control methods have been proposed to enable fine-grained, continuous, and flexible control over free-form text prompts. However, these methods not only require intensive training time and GPU memory usage to learn the sliders or embeddings but also need to be retrained for different diffusion backbones, limiting their scalability and adaptability. To address these limitations, we introduce Text Slider, a lightweight, efficient and plug-and-play framework that identifies low-rank directions within a pre-trained text encoder, enabling continuous control of visual concepts while significantly reducing training time, GPU memory consumption, and the number of trainable parameters. Furthermore, Text Slider supports multi-concept composition and continuous control, enabling fine-grained and flexible manipulation in both image and video synthesis. We show that Text Slider enables smooth and continuous modulation of specific attributes while preserving the original spatial layout and structure of the input. Text Slider achieves significantly better efficiency: 5$\times$ faster training than Concept Slider and 47$\times$ faster than Attribute Control, while reducing GPU memory usage by nearly 2$\times$ and 4$\times$, respectively.

Even though current generation of AI is underpinned by a common technology - namely machine learning, especially in the form of deep learning - in the public eye it has not emerged as a single solution. Rather, it has taken shape through multiple and overlapping applications - ranging from predictive diagnostics in healthcare and algorithmic hiring systems in HR to autonomous weapons and generative language models. As AI becomes increasingly embedded in sector - specific infrastructures, the question of how publics perceive its us e is gaining urgency. Existing literature on public perception of AI suggests that attitudes are highly sensitive to the application domain . People tend to be more supportive of AI in domains where it is perceived to augment human capacity (e.g., in medical diagnostics) and more sceptical when AI is seen as replacing judg e ment or threatening civil liberties or rights (e.g., in security or surveillance). These perceptions are shaped not only by technical features of the AI system but also by institutional trust, cultural attitude s toward risk, and the moral economy of the domain in question. Despite this, few reviews have systematically compared public perceptions across sectors and explored the cross - domain patterns and differences in attitudes.

We present Qianfan-VL, a series of multimodal large language models ranging from 3B to 70B parameters, achieving state-of-the-art performance through innovative domain enhancement techniques. Our approach employs multi-stage progressive training and high-precision data synthesis pipelines, which prove to be critical technologies for enhancing domain-specific capabilities while maintaining strong general performance. Qianfan-VL achieves comparable results to leading open-source models on general benchmarks, with state-of-the-art performance on benchmarks such as CCBench, SEEDBench IMG, ScienceQA, and MMStar. The domain enhancement strategy delivers significant advantages in OCR and document understanding, validated on both public benchmarks (OCRBench 873, DocVQA 94.75%) and in-house evaluations. Notably, Qianfan-VL-8B and 70B variants incorporate long chain-of-thought capabilities, demonstrating superior performance on mathematical reasoning (MathVista 78.6%) and logical inference tasks. All models are trained entirely on Baidu's Kunlun P800 chips, validating the capability of large-scale AI infrastructure to train SOTA-level multimodal models with over 90% scaling efficiency on 5000 chips for a single task. This work establishes an effective methodology for developing domain-enhanced multimodal models suitable for diverse enterprise deployment scenarios.