ChatGPT on macOS is about to become more useful for coding. ChatGPT can now edit code directly within an integrated development environment -- no need to copy and paste. You can find the full list of supported IDEs on OpenAI's website, but some of the more notable inclusions are Apple's own Xcode, Visual Code Studio and offshoots of Jetbrains like Android Studio and PyCharm. According to OpenAI, IDE integration has been one of the most-requested features from macOS users since the company released its "works with app" framework back in November. If you're a Plus, Pro or Team subscriber, you can start using the integration today.

Trump counselor Alina Habba responds to concerns of China buying up American real estate on'The Ingraham Angle.' State attorneys general have joined the growing calls from elected officials urging Congress to pass a law banning the Chinese-owned DeepSeek AI app on all government devices, saying "China is a clear and present danger" to the U.S. "DeepSeek appears to be another tool for Chinese spies to attack America's national security," the letter, signed by 21 attorneys general to House and Senate leaders, said. "Given the Chinese desire to steal America's secrets and the ability of DeepSeek to carry out this theft, Congress should quickly pass legislation to ban DeepSeek on government devices," the letter read. "Congress passed similar legislation two years ago to prevent TikTok from stealing information from our government." Montana AG Austin Knudsen, who drafted the letter, wrote that "China is trying to steal America's secrets. Congress should shut down China's latest Trojan horse by passing the No DeepSeek on Government Devices Act."

While the improvements feel as incremental as its name suggests, GPT-4.5 is still OpenAI's most ambitious drop to date. Released in late February as a research preview--which essentially means OpenAI sees this as a beta version--GPT-4.5 uses more computing power than its previous models and was trained on more data. So, just how big is the GPT-4.5 research preview? And where did this additional training data come from? Their lips are zipped on that as well.

Charles Darwin chatting with students about evolution, primary school pupils seeing their writing transformed into images, Luton reimagined as a cool automobile – artificial intelligence is invading schools across England in surprising ways. While Bridget Phillipson, the education secretary, in January called for a "digital revolution" involving AI in schools, it has already begun in places such as Willowdown primary school in Bridgwater, Somerset. Matt Cave, Willowdown's head teacher, said his pupils improve their descriptive writing by feeding their work into an AI client to generate images. "All of a sudden they've got all these pictures from different people's descriptions, and they can then discuss with their classmates whether that was the image they expected to be in the reader's head," Cave said. "It was really stimulating and thought-provoking for them to have a different audience."

The rise of generative artificial intelligence (AI) as a novel frontier that uniquely merges advanced levels of intelligence with revolutionary user experiences is redefining the AI landscape for future cellular networks. In particular, the transition towards 6G systems has introduced a myriad of challenges inherent to their AI-native network design, requiring innovative solutions to enable real-time network orchestration, intelligent decision-making, and adaptive dynamic configurations. Meanwhile, the envisioned user experiences for 6G are growing increasingly complex, exceeding the capabilities offered by vintage wireless technologies and conventional AI solutions to satisfy their advanced demands. With its disruptive impact evident across diverse fields, generative AI possesses immense potential to tackle these challenges, leveraging its exceptional capabilities to manage complex tasks, operate autonomously, and adapt seamlessly to scenarios beyond its training domain. Remarkably, generative AI provides a transformative opportunity for telecom and cellular networks to bridge this defined gap in 6G systems, thereby shifting towards a new era with cutting-edge AI innovations across the different system and user levels.

Recent advances demonstrate that increasing inference-time computation can significantly boost the reasoning capabilities of large language models (LLMs). Although repeated sampling (i.e., generating multiple candidate outputs) is a highly effective strategy, it does not leverage external feedback signals for refinement, which are often available in tasks like coding. In this work, we propose Adaptive Branching Monte Carlo Tree Search (AB-MCTS), a novel inference-time framework that generalizes repeated sampling with principled multi-turn exploration and exploitation. At each node in the search tree, AB-MCTS dynamically decides whether to "go wider" by expanding new candidate responses or "go deeper" by revisiting existing ones based on external feedback signals. We evaluate our method on complex coding and engineering tasks using frontier models. Empirical results show that AB-MCTS consistently outperforms both repeated sampling and standard MCTS, underscoring the importance of combining the response diversity of LLMs with multi-turn solution refinement for effective inference-time scaling. Recent work (Li et al., 2022; Lewkowycz et al., 2022; Brown et al., 2024; Wu et al., 2025) has begun to reveal that scaling inference-time computation can significantly boost the performance of large language models (LLMs) on complex tasks. Traditionally, LLM performance improvements have stemmed from training-time scaling--namely, increasing the size of training datasets, model parameters, and computational resources at training (Kaplan et al., 2020; Hoffmann et al., 2022). In contrast, inference-time scaling seeks to improve the performance of an LLM by allocating more computational resources at inference. As we outline in Section 2, there are broadly three types of approaches to achieve inference-time scaling: post-training tuning, reward-guided CoT (Chain-of-Thought), and multiple answer generation.

While state-of-the-art language models achieve impressive results through next-token prediction, they have inherent limitations such as the inability to revise already generated tokens. This has prompted exploration of alternative approaches such as discrete diffusion. However, masked diffusion, which has emerged as a popular choice due to its simplicity and effectiveness, reintroduces this inability to revise words. To overcome this, we generalize masked diffusion and derive the theoretical backbone of a family of general interpolating discrete diffusion (GIDD) processes offering greater flexibility in the design of the noising processes. Leveraging a novel diffusion ELBO, we achieve compute-matched state-of-the-art performance in diffusion language modeling. Exploiting GIDD's flexibility, we explore a hybrid approach combining masking and uniform noise, leading to improved sample quality and unlocking the ability for the model to correct its own mistakes, an area where autoregressive models notoriously have struggled. Our code and models are open-source: https://github.com/dvruette/gidd/

Large Language Models (LLMs) are rapidly saturating existing benchmarks, necessitating new open-ended evaluations. We introduce the Factorio Learning Environment (FLE), based on the game of Factorio, that tests agents in long-term planning, program synthesis, and resource optimization. FLE provides exponentially scaling challenges -- from basic automation to complex factories processing millions of resource units per second. We provide two settings: (1) lab-play consisting of eight structured tasks with fixed resources, and (2) open-play with the unbounded task of building the largest factory on an procedurally generated map. We demonstrate across both settings that models still lack strong spatial reasoning. In lab-play, we find that LLMs exhibit promising short-horizon skills, yet are unable to operate effectively in constrained environments, reflecting limitations in error analysis. In open-play, while LLMs discover automation strategies that improve growth (e.g electric-powered drilling), they fail to achieve complex automation (e.g electronic-circuit manufacturing).

Privacy-preserving medical decision support for kidney disease requires localized deployment of large language models (LLMs) while maintaining clinical reasoning capabilities. Current solutions face three challenges: 1) Cloud-based LLMs pose data security risks; 2) Local model deployment demands technical expertise; 3) General LLMs lack mechanisms to integrate medical knowledge. Retrieval-augmented systems also struggle with medical document processing and clinical usability. We developed KidneyTalk-open, a desktop system integrating three technical components: 1) No-code deployment of state-of-the-art (SOTA) open-source LLMs (such as DeepSeek-r1, Qwen2.5) via local inference engine; 2) Medical document processing pipeline combining context-aware chunking and intelligent filtering; 3) Adaptive Retrieval and Augmentation Pipeline (AddRep) employing agents collaboration for improving the recall rate of medical documents. A graphical interface was designed to enable clinicians to manage medical documents and conduct AI-powered consultations without technical expertise. Experimental validation on 1,455 challenging nephrology exam questions demonstrates AddRep's effectiveness: achieving 29.1% accuracy (+8.1% over baseline) with intelligent knowledge integration, while maintaining robustness through 4.9% rejection rate to suppress hallucinations. Comparative case studies with the mainstream products (AnythingLLM, Chatbox, GPT4ALL) demonstrate KidneyTalk-open's superior performance in real clinical query. KidneyTalk-open represents the first no-code medical LLM system enabling secure documentation-enhanced medical Q&A on desktop. Its designs establishes a new framework for privacy-sensitive clinical AI applications. The system significantly lowers technical barriers while improving evidence traceability, enabling more medical staff or patients to use SOTA open-source LLMs conveniently.

Many less-resourced languages struggle with a lack of large, task-specific datasets that are required for solving relevant tasks with modern transformer-based large language models (LLMs). On the other hand, many linguistic resources, such as dictionaries, are rarely used in this context despite their large information contents. We show how LLMs can be used to extend existing language resources in less-resourced languages for two important tasks: word-sense disambiguation (WSD) and word-sense induction (WSI). We approach the two tasks through the related but much more accessible word-in-context (WiC) task where, given a pair of sentences and a target word, a classification model is tasked with predicting whether the sense of a given word differs between sentences. We demonstrate that a well-trained model for this task can distinguish between different word senses and can be adapted to solve the WSD and WSI tasks. The advantage of using the WiC task, instead of directly predicting senses, is that the WiC task does not need pre-constructed sense inventories with a sufficient number of examples for each sense, which are rarely available in less-resourced languages. We show that sentence pairs for the WiC task can be successfully generated from dictionary examples using LLMs. The resulting prediction models outperform existing models on WiC, WSD, and WSI tasks. We demonstrate our methodology on the Slovene language, where a monolingual dictionary is available, but word-sense resources are tiny.