Loops--short audio segments designed for seamless repetition--are central to many music genres, particularly those rooted in dance and electronic styles. However, current generative music models struggle to produce truly loopable audio, as generating a short waveform alone does not guarantee a smooth transition from its endpoint back to its start, often resulting in audible discontinuities. We address this gap by modifying a non-autoregressive model (MAGNeT) to generate tokens in a circular pattern, letting the model attend to the beginning of the audio when creating its ending. This inference-only approach results in generations that are aware of future context and loop naturally, without the need for any additional training or data. We evaluate the consistency of loop transitions by computing token perplexity around the seam of the loop, observing a 55% improvement. Blind listening tests further confirm significant perceptual gains over baseline methods, improving mean ratings by 70%. Taken together, these results highlight the effectiveness of inference-only approaches in improving generative models and underscore the advantages of non-autoregressive methods for context-aware music generation.

Language is a fundamental aspect of human societies, continuously evolving in response to various stimuli, including societal changes and intercultural interactions. Technological advancements have profoundly transformed communication, with social media emerging as a pivotal force that merges entertainment-driven content with complex social dynamics. As these platforms reshape public discourse, analyzing the linguistic features of user-generated content is essential to understanding their broader societal impact. In this paper, we examine the linguistic complexity of content produced by influential users on Twitter across three globally significant and contested topics: COVID-19, COP26, and the Russia-Ukraine war. By combining multiple measures of textual complexity, we assess how language use varies along four key dimensions: account type, political leaning, content reliability, and sentiment. Our analysis reveals significant differences across all four axes, including variations in language complexity between individuals and organizations, between profiles with sided versus moderate political views, and between those associated with higher versus lower reliability scores. Additionally, profiles producing more negative and offensive content tend to use more complex language, with users sharing similar political stances and reliability levels converging toward a common jargon. Our findings offer new insights into the sociolinguistic dynamics of digital platforms and contribute to a deeper understanding of how language reflects ideological and social structures in online spaces.

This study focuses on evaluating the performance of machine translations (MTs) compared to human translations (HTs) in English-to-Chinese children's literature translation (CLT) from a stylometric perspective. The research constructs a Peter Pan corpus, comprising 21 translations: 7 human translations (HTs), 7 large language model translations (LLMs), and 7 neural machine translation outputs (NMTs). The analysis employs a generic feature set (including lexical, syntactic, readability, and n-gram features) and a creative text translation (CTT-specific) feature set, which captures repetition, rhythm, translatability, and miscellaneous levels, yielding 447 linguistic features in total. Using classification and clustering techniques in machine learning, we conduct a stylometric analysis of these translations. Results reveal that in generic features, HTs and MTs exhibit significant differences in conjunction word distributions and the ratio of 1-word-gram-YiYang, while NMTs and LLMs show significant variation in descriptive words usage and adverb ratios. Regarding CTT-specific features, LLMs outperform NMTs in distribution, aligning more closely with HTs in stylistic characteristics, demonstrating the potential of LLMs in CLT.

This paper introduces KunLunBaizeRAG, a reinforcement learning-driven reasoning framework designed to enhance the reasoning capabilities of large language models (LLMs) in complex multi-hop question-answering tasks. The framework addresses key limitations of traditional RAG, such as retrieval drift, information redundancy, and strategy rigidity. Key innovations include the RAG-driven Reasoning Alignment (RDRA) mechanism, the Search-Think Iterative Enhancement (STIE) mechanism, the Network-Local Intelligent Routing (NLR) mechanism, and a progressive hybrid training strategy. Experimental results demonstrate significant improvements in exact match (EM) and LLM-judged score (LJ) across four benchmarks, highlighting the framework's robustness and effectiveness in complex reasoning scenarios.

Breakthroughs, discoveries, and DIY tips sent every weekday. When classic films undergo 4K restorations, the results can divide fans. Look around Hollywood and you'll find numerous examples of movie rereleases featuring controversial uses of digital noise reduction, motion smoothing, and other post-production tools. Meanwhile, the proliferation of AI- and machine learning-based upscaling programs has only complicated the debate. When approached properly, though, the technique has helped revive some of Hollywood's oldest--and for a long time, inaccessible--movies.

A few hundred robots moved a buildng complex covering about 43,400 square feet. Shanghai is no stranger to jaw-dropping feats of engineering. In the latest example, a Shanghai historic building moved by robots is capturing global attention. The relocation of the complex in Huayang, a Shikumen-style building weighing about 7,500 metric tons (approximately 8,267 U.S. tons) and covering roughly 43,400 square feet, is truly rewriting the rules. This ambitious project is powered by an army of 432 small robots that are moving the massive structure about 33 feet each day to make way for a new underground development.

This enterprise proved so amusing that the pair, in collaboration with composer Dave Soldier, repeated the experiment with popular music, releasing the "most wanted" and "least wanted" songs together on a CD with a cover photo of all three men wearing white lab coats and pointing at a calculator. Sadly, the pair stopped short of what I view as the greatest challenge: producing novels that reflect what Americans like and dislike in fiction. Now, at last, with People's Choice Literature, by the writer/artist/composer Tom Comitta, a new "scientist" has taken up the task. People's Choice Literature offers its readers two novels for the price of one. The first is a thriller whose heroine tries to prevent her boss, a new age–y tech mogul, from launching a quantum computing network that will bring about a total surveillance state.

Amazon Prime Day 2025 is fast approaching, and the sale is already underway on some items. To help you find the best early Prime Day deals, we've scoured Amazon for deals on the tech we love. As always, every deal we recommend here is on a product our reviewers have personally tested and approved--you won't find any shoddy dupes or mystery brands here. This year Prime Day runs for four days, July 8-11, rather than the usual two. That means there's twice as long to suffer save. Be sure to read our explainer on all the Amazon Prime perks you should be taking advantage of.

'The CyberGuy' Kurt Knutsson joins'Fox & Friends Weekend' to discuss the potential effects of artificial intelligence software like ChatGPT on the brain. Massachusetts Institute of Technology researchers are studying ChatGPT's effects on the brain. BRAIN DANGER: Using ChatGPT on a long-term basis could have negative effects on brain function. That's according to a study led by the Massachusetts Institute of Technology (MIT), which found that using a large language model (LLM) to write multiple essays over a four-month period could hamper cognitive abilities. 'ERRATIC': Videos taken this week by passengers showed Tesla robotaxis – which are Model Y vehicles with advanced software – braking suddenly, speeding, conducting improper drop-offs, entering the wrong lane and driving over a curb, according to Reuters.

Corey Feldman discusses his movie "The Birthday," which wrapped in 2004. "The Goonies" star Corey Feldman is concerned that the rise of artificial intelligence could ruin the "magic" of Hollywood filmmaking. In a new interview with Fox News Digital, the entertainer talked about his decades of being part of the film industry and what he thinks of it today compared to how it was when he was starring in beloved 80s classics like "Goonies," "The Lost Boys" and "The Burbs." When asked if he believes modern Hollywood can still conjure up the same "magic" that led to the creation of these iconic films, he said he wasn't so sure. "Well, I share the opinion that there is a lot of the magic that's been lost because of A.I., because of CGI, because of, you know, these things kind of taking over from the good stories, the great characters that we draw, the great writing," Feldman said.