Mickey Mouse and Minnie Mouse floats at the Magic Kingdom Park at Walt Disney World in Orlando, Florida, on 3 April 2025. Mickey Mouse and Minnie Mouse floats at the Magic Kingdom Park at Walt Disney World in Orlando, Florida, on 3 April 2025. Walt Disney has announced a $1bn equity investment in OpenAI, enabling the AI start-up's Sora video generation tool to use its characters. Users of Sora will be able to generate short, user-prompted social videos that draw on more than 200 Disney, Marvel, Pixar and Star Wars characters as part of a three-year licensing agreement between OpenAI and the entertainment giant. A selection of the videos made by users will also be available for streaming on the Disney+ platform. Bob Iger, Disney's CEO, hailed a deal which paired his firm's "iconic stories and characters" with OpenAI's AI technology.

'Architects of AI' named Time Magazine's Person of the Year Time Magazine's Person of the Year for 2025 is not a single person. Instead, the magazine has recognised the year's most influential figure as the architects of artificial intelligence (AI). Nvidia boss Jensen Huang, Meta head Mark Zuckerberg, X owner Elon Musk and AI godmother Fei-Fei Li are among those depicted on one of the magazine's two covers. Experts say it highlights how quickly AI, and the firms behind it, are reshaping society. It comes as a boom in the technology, ushered in by OpenAI's launch of ChatGPT in late 2022, continues at pace.

The Download: solar geoengineering's future, and OpenAI is being sued Solar geoengineering aims to manipulate the climate by bouncing sunlight back into space. In theory, it could ease global warming. But as interest in the idea grows, so do concerns about potential consequences. A startup called Stardust Solutions recently raised a $60 million funding round, the largest known to date for a geoengineering startup. My colleague James Temple has a new story out about the company, and how its emergence is making some researchers nervous. So far, the field has been limited to debates, proposed academic research, and--sure--a few fringe actors to keep an eye on.

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The heirs of an 83-year-old Connecticut mother who was killed by her son in August filed a lawsuit against OpenAI and Microsoft, claiming ChatGPT amplified his "paranoid delusions."

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This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .

Ghislaine Maxwell's ultimate humiliation: Epstein's sex trafficker girlfriend poses in outrageous outfits and exposes herself in dozens of photos released from the billionaire paedophile's files I was falsely accused of being the Brown University shooter... Silent Trump flees growing storm over Epstein'cover-up' as he jets off for holidays without ANY comment Truth about THIS photo of Karoline Leavitt's face... and why if she was non-binary and disabled, Vanity Fair would never have done this: KENNEDY Why Conan O'Brien'stopped party guests calling 911' on Nick Reiner: Insiders reveal disturbing new details of final hours before Rob and Michele murders After 27 years as a TV anchor I was suddenly pulled off screens. My boss's explanation was a brutal lesson in loyalty Emily in Paris cast left'aghast' and'walking on eggshells' as off-camera drama becomes overwhelming... and whispers swirl about a CURSE Doctors said my hip pain was just tendinitis from sitting all day at work.

This article presents the creation of an Estonian-language dataset for document-level subjectivity, analyzes the resulting annotations, and reports an initial experiment of automatic subjectivity analysis using a large language model (LLM). The dataset comprises of 1,000 documents-300 journalistic articles and 700 randomly selected web texts-each rated for subjectivity on a continuous scale from 0 (fully objective) to 100 (fully subjective) by four annotators. As the inter-annotator correlations were moderate, with some texts receiving scores at the opposite ends of the scale, a subset of texts with the most divergent scores was re-annotated, with the inter-annotator correlation improving. In addition to human annotations, the dataset includes scores generated by GPT-5 as an experiment on annotation automation. These scores were similar to human annotators, however several differences emerged, suggesting that while LLM based automatic subjectivity scoring is feasible, it is not an interchangeable alternative to human annotation, and its suitability depends on the intended application.

Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge into Large Language Models (LLMs), typically from unstructured texts and structured graphs. While recent progress has advanced text-based RAG to multi-turn reasoning through Reinforcement Learning (RL), extending these advances to hybrid retrieval introduces additional challenges. Existing graph-based or hybrid systems typically depend on fixed or handcrafted retrieval pipelines, lacking the ability to integrate supplementary evidence as reasoning unfolds. Besides, while graph evidence provides relational structures crucial for multi-hop reasoning, it is substantially more expensive to retrieve. To address these limitations, we introduce \model{}, an RL-based framework that enables LLMs to perform multi-turn and adaptive graph-text hybrid RAG. \model{} jointly optimizes the entire generation process via RL, allowing the model to learn when to reason, what to retrieve from either texts or graphs, and when to produce final answers, all within a unified generation policy. To guide this learning process, we design a two-stage training framework that accounts for both task outcome and retrieval efficiency, enabling the model to exploit hybrid evidence while avoiding unnecessary retrieval overhead. Experimental results across five question answering benchmarks demonstrate that \model{} significantly outperforms existing RAG baselines, highlighting the benefits of end-to-end RL in supporting adaptive and efficient retrieval for complex reasoning.