Granta said it would no longer be involved in'external publishing partnerships' in which it had no editorial control. Granta said it would no longer be involved in'external publishing partnerships' in which it had no editorial control. Literary magazine will no longer engage in'external publishing partnerships' after Commonwealth prize furore The prominent literary magazine Granta will no longer publish the winning entries of the annual Commonwealth short story prize after one of this year's winners drew widespread accusations of AI use. The magazine said it would no longer be involved in "external publishing partnerships" in which it had no editorial control. In a statement to the Guardian, Granta said: "The 2026 selection of the regional winners of the Commonwealth prize caused a great deal of controversy, based on the speculation that one or more of the stories may have been at least partially AI-generated, accusations that were strongly rejected by the authors. "For the sake of our own editorial integrity, the Granta Trust board has now taken the decision that we will no longer engage in external publishing partnerships.

The draft revision of the Artificial Intelligence Basic Plan highlights the growing risk of cyberattacks that exploit AI.

What happens if pretending to be someone else becomes your entire life? It is a question at the heart of many of the biggest spy dramas, from Slow Horses to Black Doves - and it is one that TV thriller series The Agency explores more deeply than most. Returning for a second season, the Paramount+ thriller follows CIA operatives living under deep-cover identities. It examines not just the dangers of espionage, but the psychological cost of maintaining a lie for years. Starring Michael Fassbender, Richard Gere and Katherine Waterston, the series is based on acclaimed French drama The Bureau.

We analyse how the sampling dynamics of distributions evolve in score-based diffusion models using cross-fluctuations, a centered-moment statistic from statistical physics. Specifically, we show that starting from an unbiased isotropic normal distribution, samples undergo sharp, discrete transitions, eventually forming distinct events of a desired distribution while progressively revealing finer structure. As this process is reversible, these transitions also occur in reverse, where intermediate states progressively merge, tracing a path back to the initial distribution. We demonstrate that these transitions can be detected as discontinuities in nth-order cross-fluctuations. For variance-preserving SDEs, we derive a closed-form for these cross-fluctuations that is efficiently computable for the reverse trajectory. We find that detecting these transitions directly boosts sampling efficiency, accelerates class-conditional and rare-class generation, and improves two zero-shot tasks-image classification and style transfer-without expensive grid search or retraining. We also show that this viewpoint unifies classical coupling and mixing from finite Markov chains with continuous dynamics while extending to stochastic SDEs and non Markovian samplers.

Retrieval-Augmented Generation (RAG) provides additional contextual knowledge to complement the parametric knowledge in Large Language Models (LLMs). These two knowledge interweave to enhance the accuracy and timeliness of LLM responses. However, the internal mechanisms by which LLMs utilize these knowledge remain unclear. We propose modeling the forward propagation of knowledge as an entity flow, employing this framework to trace LLMs' internal behaviors when processing mixed-source knowledge. Linear probing utilizes a trainable linear classifier to detect specific attributes in hidden layers.

Recent advances in large language models (LLMs) and audio language models have significantly improved music generation, particularly in lyrics-to-song generation.

Traditional image editing typically relies on manual prompting, making it laborintensive and inaccessible to individuals with limited motor control or language abilities. Leveraging recent advances in brain-computer interfaces (BCIs) and generative models, we propose LoongX, a hands-free image editing approach driven by multimodal neurophysiological signals. LoongX utilizes state-of-the-art diffusion models trained on a comprehensive dataset of 23,928 image editing pairs, each paired with synchronized electroencephalography (EEG), functional nearinfrared spectroscopy (fNIRS), photoplethysmography (PPG), and head motion signals that capture user intent. To effectively address the heterogeneity of these signals, LoongX integrates two key modules.

Finetuning provides a scalable and cost-effective means of customizing language models for specific tasks or response styles, with greater reliability than prompting or in-context learning. In contrast, the conventional wisdom is that injecting knowledge via finetuning results in brittle performance and poor generalization. We argue that the dichotomy of "task customization" (e.g., instruction tuning) and "knowledge injection" (e.g., teaching new facts) is a distinction without a difference. We instead identify concrete factors that explain the heterogeneous effectiveness observed with finetuning. To this end, we conduct a large-scale experimental study of finetuning the frontier Gemini v1.5 model family on a spectrum of datasets that are artificially engineered to interpolate between the strengths and failure modes of finetuning. Our findings indicate that question-answer training data formats provide much stronger knowledge generalization than document/articlestyle training data, numerical information can be harder for finetuning to retain than categorical information, and models struggle to apply finetuned knowledge during multi-step reasoning even when trained on similar examples--all factors that render "knowledge injection" to be especially difficult, even after controlling for considerations like data augmentation and information volume. On the other hand, our findings also indicate that it is not fundamentally more difficult to finetune information about a real-world event than information about writing style.

Amazon founder Jeff Bezos delivers a harsh prediction on the future of AI in the workforce, plus OpenAI faces an investigation into data handling and chatbot behavior.

While being disturbed by environmental noises, the acoustic masking technique is a conventional way to reduce the annoyance in audio engineering that seeks to cover up the noises with other dominant yet less intrusive sounds. However, misalignment between the dominant sound and the noise--such as mismatched downbeats--often requires an excessive volume increase to achieve effective masking. Motivated by recent advances in cross-modal generation, in this work, we introduce an alternative method to acoustic masking, aiming to reduce the noticeability of environmental noises by blending them into personalized music generated based on user-provided text prompts. Following the paradigm of music generation using mel-spectrogram representations, we propose a Blending Noises into Personalized Music (BNMusic) framework with two key stages.