Jamie Smart (UK) portrays a red deer stag as it gives a mighty bellow during the autumn rut in Bradgate Park, UK. Breakthroughs, discoveries, and DIY tips sent every weekday. Hunting is a crucial skill for young cheetahs . Photographer Marina Cano captured the intense moment before the siblings killed the prey in a stunning image (seen below) that that took Highly Commended honors in the Mammals: Behavior category at the Wildlife Photographer of the Year awards. The prestigious competition is now in its 61st year and is developed and produced by the Natural History Museum, London.

You can do more with your pictures--you just need the right app. Breakthroughs, discoveries, and DIY tips sent every weekday. You've likely got a plethora of photos stored on your phone, charting out every day (or maybe even every hour) in detail, but have you done anything with them lately? Maybe you share them just as they are--or perhaps you leave them locked away. The right photo editing app can help you to do more with your photos, and make the most of them before sharing.

Fox News Flash top headlines are here. Check out whats clicking on Foxnews.com. Rental car drivers are now turning to artificial intelligence to protect themselves from surprise damage fees. Major companies, such as Hertz and Sixt, have begun using automated inspection tools to detect scratches and dents. While these scanners promise efficiency, they have sparked backlash from renters who say they were unfairly billed for minor blemishes.

When the chief executive of the Financial Times suggested at a media conference this summer that rival publishers might consider a "Nato for news" alliance to strengthen negotiations with artificial intelligence companies there was a ripple of chuckles from attendees. Yet Jon Slade's revelation that his website had seen a "pretty sudden and sustained" decline of 25% to 30% in traffic to its articles from readers arriving via internet search engines quickly made clear the serious nature of the threat the AI revolution poses. Queries typed into sites such as Google, which accounts for more than 90% of the search market, have been central to online journalism since its inception, with news providers optimising headlines and content to ensure a top ranking and revenue-raising clicks. But now Google's AI Overviews, which sit at the top of the results page and summarise responses and often negate the need to follow links to content, as well as its recently launched AI Mode tab that answers queries in a chatbot format, have prompted fears of a "Google zero" future where traffic referrals dry up. "This is the single biggest change to search I have seen in decades," says one senior editorial tech executive.

An AI company is to reconstruct the missing portions of Orson Welles' legendary mutilated masterwork The Magnificent Ambersons, it has been announced. According to the Hollywood Reporter, the Showrunner platform is planning to use its AI tools to assist in a recreation of the lost 43 minutes of Welles' 1942 film, removed and subsequently destroyed by Hollywood studio RKO. Edward Saatchi, CEO of interactive AI film-making studio Fable, which operates Showrunner, said in a statement to IndieWire: "We're starting with Orson Welles because he is the greatest storyteller of the last 200 years … So many people are rightly skeptical of AI's impact on cinema – but we hope that this gives people a sense of a positive contribution that AI can make for storytelling." Reports suggest that Showrunner is partnering with film-maker Brian Rose, who has been working since 2019 on an attempt to reconstruct the missing portions using animated sequences, as well as VFX expert Tom Clive. Welles started production in 1942 on The Magnificent Ambersons, an adaptation of Booth Tarkington's celebrated novel about a midwestern family in decline, as a follow-up to his Oscar-winning debut Citizen Kane.

In the largest cinema at the Venice film festival, guests gather for the premiere of Frankenstein, Guillermo del Toro's lavish account of a man who dared to play God and created a monster. When the young scientist reanimates a dead body for his colleagues, some see it as a trick while others are outraged. "It's an abomination, an obscenity," shouts one hide-bound old timer, and his alarm is partly justified. Every technological breakthrough opens Pandora's box. You don't know what's going to crawl out or where it will then choose to go.

Understanding covert narratives and implicit messaging is essential for analyzing bias and sentiment. Traditional NLP methods struggle with detecting subtle phrasing and hidden agendas. This study tackles two key challenges: (1) multi-label classification of narratives and sub-narratives in news articles, and (2) generating concise, evidence-based explanations for dominant narratives. We fine-tune a BERT model with a recall-oriented approach for comprehensive narrative detection, refining predictions using a GPT-4o pipeline for consistency. For narrative explanation, we propose a ReACT (Reasoning + Acting) framework with semantic retrieval-based few-shot prompting, ensuring grounded and relevant justifications. To enhance factual accuracy and reduce hallucinations, we incorporate a structured taxonomy table as an auxiliary knowledge base. Our results show that integrating auxiliary knowledge in prompts improves classification accuracy and justification reliability, with applications in media analysis, education, and intelligence gathering.

The effectiveness of large language models (LLMs) to fact-check misinformation remains uncertain, despite their growing use. To this end, we present CANDY, a benchmark designed to systematically evaluate the capabilities and limitations of LLMs in fact-checking Chinese misinformation. Specifically, we curate a carefully annotated dataset of ~20k instances. Our analysis shows that current LLMs exhibit limitations in generating accurate fact-checking conclusions, even when enhanced with chain-of-thought reasoning and few-shot prompting. To understand these limitations, we develop a taxonomy to categorize flawed LLM-generated explanations for their conclusions and identify factual fabrication as the most common failure mode. Although LLMs alone are unreliable for fact-checking, our findings indicate their considerable potential to augment human performance when deployed as assistive tools in scenarios. Our dataset and code can be accessed at https://github.com/SCUNLP/CANDY

Recent significant advancements in Large Language Models (LLMs) have greatly propelled the development of Role-Playing Conversational Agents (RPCAs). These systems aim to create immersive user experiences through consistent persona adoption. However, current RPCA research faces dual limitations. First, existing work predominantly focuses on the textual modality, entirely overlooking critical paralinguistic features including intonation, prosody, and rhythm in speech, which are essential for conveying character emotions and shaping vivid identities. Second, the speech-based role-playing domain suffers from a long-standing lack of standardized evaluation benchmarks. Most current spoken dialogue datasets target only fundamental capability assessments, featuring thinly sketched or ill-defined character profiles. Consequently, they fail to effectively quantify model performance on core competencies like long-term persona consistency. To address this critical gap, we introduce VoxRole, the first comprehensive benchmark specifically designed for the evaluation of speech-based RPCAs. The benchmark comprises 13335 multi-turn dialogues, totaling 65.6 hours of speech from 1228 unique characters across 261 movies. To construct this resource, we propose a novel two-stage automated pipeline that first aligns movie audio with scripts and subsequently employs an LLM to systematically build multi-dimensional profiles for each character. Leveraging VoxRole, we conduct a multi-dimensional evaluation of contemporary spoken dialogue models, revealing crucial insights into their respective strengths and limitations in maintaining persona consistency.

Smartphones have become indispensable in people's daily lives, permeating nearly every aspect of modern society. With the continuous advancement of large language models (LLMs), numerous LLM-based mobile agents have emerged. These agents are capable of accurately parsing diverse user queries and automatically assisting users in completing complex or repetitive operations. However, current agents 1) heavily rely on the comprehension ability of LLMs, which can lead to errors caused by misoperations or omitted steps during tasks, 2) lack interaction with the external environment, often terminating tasks when an app cannot fulfill user queries, and 3) lack memory capabilities, requiring each instruction to reconstruct the interface and being unable to learn from and correct previous mistakes. To alleviate the above issues, we propose MobileRAG, a mobile agents framework enhanced by Retrieval-Augmented Generation (RAG), which includes InterRAG, LocalRAG, and MemRAG. It leverages RAG to more quickly and accurately identify user queries and accomplish complex and long-sequence mobile tasks. Additionally, to more comprehensively assess the performance of MobileRAG, we introduce MobileRAG-Eval, a more challenging benchmark characterized by numerous complex, real-world mobile tasks that require external knowledge assistance. Extensive experimental results on MobileRAG-Eval demonstrate that MobileRAG can easily handle real-world mobile tasks, achieving 10.3% improvement over state-of-the-art methods with fewer operational steps. Our code is publicly available at: https://github.