Listen to more stories on the Noa app. Bill Lowery, a sales executive, is confused when a workmate asks where he should take a date out for dinosaur. "You're planning to take this girl out for?" "That's right," the colleague responds, totally nonchalant. Lowery presses him, agitated: "Wait a minute. What is this, some sort of new-wave expression or something--saying instead of?" "He's so pale and awfully congested--and he didn't touch his dinosaur when I took it in to him."

A group of quantum physicists claims to have created a version of the powerful reasoning AI model DeepSeek R1 that strips out the censorship built into the original by its Chinese creators. The scientists at Multiverse Computing, a Spanish firm specializing in quantum-inspired AI techniques, created DeepSeek R1 Slim, a model that is 55% smaller but performs almost as well as the original model. Crucially, they also claim to have eliminated official Chinese censorship from the model. In China, AI companies are subject to rules and regulations meant to ensure that content output aligns with laws and "socialist values." As a result, companies build in layers of censorship when training the AI systems.

This paper introduces Recurrent Expansion (RE) as a new learning paradigm that advances beyond conventional Machine Learning (ML) and Deep Learning (DL). While DL focuses on learning from static data representations, RE proposes an additional dimension: learning from the evolving behavior of models themselves. RE emphasizes multiple mappings of data through identical deep architectures and analyzes their internal representations (i.e., feature maps) in conjunction with observed performance signals such as loss. By incorporating these behavioral traces, RE enables iterative self-improvement, allowing each model version to gain insight from its predecessors. The framework is extended through Multiverse RE (MVRE), which aggregates signals from parallel model instances, and further through Heterogeneous MVRE (HMVRE), where models of varying architectures contribute diverse perspectives. A scalable and adaptive variant, Sc-HMVRE, introduces selective mechanisms and scale diversity for real-world deployment. Altogether, RE presents a shift in DL: from purely representational learning to behavior-aware, self-evolving systems. It lays the groundwork for a new class of intelligent models capable of reasoning over their own learning dynamics, offering a path toward scalable, introspective, and adaptive artificial intelligence. A simple code example to support beginners in running their own experiments is provided in Code Availability Section of this paper.

Autoregressive Large Language Models (AR-LLMs) frequently exhibit implicit parallelism in sequential generation. Inspired by this, we introduce Multiverse, a new generative model that enables natively parallel generation. Multiverse internalizes a MapReduce paradigm, generating automatically through three stages: (i) a Map stage for adaptive task decomposition, (ii) a Process stage for parallel subtask execution, and (iii) a Reduce stage for lossless result synthesis. Next, we build a real-world Multiverse reasoning model with co-design of data, algorithm, and system, enabling rapid and seamless transfer from frontier AR-LLMs. For data creation, we develop Multiverse Curator, an automated LLM-assisted pipeline that transforms sequential reasoning chains into structured training data, avoiding costly human annotations. Algorithmically, we design Multiverse Attention to separate parallel reasoning steps while keeping compatibility with causal attention for efficient training. Systematically, we implement Multiverse Engine to support parallel inference. It features a dedicated interpreter that dynamically switches between sequential and parallel generation, triggered directly by the model. After a 3-hour fine-tuning with 1K examples, our Multiverse-32B stands as the only open-sourced non-AR model achieving performance on par with leading AR-LLMs of the same scale, evidenced by AIME24 & 25 scores of 54% and 46%, respectively. Moreover, our budget control experiments show that Multiverse-32B exhibits superior scaling, outperforming AR-LLMs by 1.87% on average using the same context length. Such scaling further leads to practical efficiency gains, achieving up to 2x speedup across varying batch sizes. We have open-sourced the entire Multiverse ecosystem, including data, model weights, engine, as well as complete data curation prompts and detailed training and evaluation recipes.

Google's quantum computing breakthrough on Monday has left the physicist who heads the project a believer in'the idea that we live in a multiverse.' 'Willow,' the tech giant's new quantum chip, succeeded in solving a computational problem so complex it would have taken today's best super-computers an estimated 10 septillion years to solve it -- vastly more than the age of our entire universe. But Google said its new quantum computer solved the puzzle'in under five minutes.' Calling Willow's performance'astonishing,' the leader and founder of Google Quantum AI team, physicist Hartmut Neven, said its high-speed result'lends credence to the notion that quantum computation occurs in many parallel universes.' Neven credited Oxford University physicist David Deutsch for proposing the theory that the successful development of quantum computing would, in effect, affirm the'many worlds interpretation' of quantum mechanics and the existence of a multiverse. Starting in the 1970s, Deutsch, in fact, had walked backwards into becoming a pioneer in the field of quantum computing, less out of interest in the technology itself, than his desire to test the multiverse theory.

Text-to-speech (TTS) systems that scale up the amount of training data have achieved significant improvements in zero-shot speech synthesis. However, these systems have certain limitations: they require a large amount of training data, which increases costs, and often overlook prosody similarity. To address these issues, we propose MultiVerse, a zero-shot multi-task TTS system that is able to perform TTS or speech style transfer in zero-shot and cross-lingual conditions. MultiVerse requires much less training data than traditional data-driven approaches. To ensure zero-shot performance even with limited data, we leverage source-filter theory-based disentanglement, utilizing the prompt for modeling filter-related and source-related representations. Additionally, to further enhance prosody similarity, we adopt a prosody modeling approach combining prompt-based autoregressive and non-autoregressive methods. Evaluations demonstrate the remarkable zero-shot multi-task TTS performance of MultiVerse and show that MultiVerse not only achieves zero-shot TTS performance comparable to data-driven TTS systems with much less data, but also significantly outperforms other zero-shot TTS systems trained with the same small amount of data. In particular, our novel prosody modeling technique significantly contributes to MultiVerse's ability to generate speech with high prosody similarity to the given prompts. Our samples are available at https://nc-ai.github.io/speech/publications/multiverse/index.html

Echoing recent calls to counter reliability and robustness concerns in machine learning via multiverse analysis, we present PRESTO, a principled framework for mapping the multiverse of machine-learning models that rely on latent representations. Although such models enjoy widespread adoption, the variability in their embeddings remains poorly understood, resulting in unnecessary complexity and untrustworthy representations. Our framework uses persistent homology to characterize the latent spaces arising from different combinations of diverse machine-learning methods, (hyper)parameter configurations, and datasets, allowing us to measure their pairwise (dis)similarity and statistically reason about their distributions. As we demonstrate both theoretically and empirically, our pipeline preserves desirable properties of collections of latent representations, and it can be leveraged to perform sensitivity analysis, detect anomalous embeddings, or efficiently and effectively navigate hyperparameter search spaces.

Put bluntly, picking the best movies of 2023 was tough. The double-whammy of Barbie and Oppenheimer gave the box office a long-overdue, post-Covid-19 jolt, only to be followed by a pair of months-long strikes in Hollywood that shut down production on nearly all the films in the works for 2024 and beyond. Even now, with the strikes over, the industry is scratching its head at what happened and what's to come. Still, amidst all the noise, 2023 provided a wealth of quietly beautiful films. Even as Hollywood fretted over the possibility of artificial intelligence upending filmmaking and giving writing and acting gigs to bots, it's impossible to watch the movies on this list and not feel such a possibility is faintly ridiculous.

Creating a digital world that closely mimics the real world with its many complex interactions and outcomes is possible today through advanced emulation software and ubiquitous computing power. Such a software-based emulation of an entity that exists in the real world is called a 'digital twin'. In this paper, we consider a twin of a wireless millimeter-wave band radio that is mounted on a vehicle and show how it speeds up directional beam selection in mobile environments. To achieve this, we go beyond instantiating a single twin and propose the 'Multiverse' paradigm, with several possible digital twins attempting to capture the real world at different levels of fidelity. Towards this goal, this paper describes (i) a decision strategy at the vehicle that determines which twin must be used given the computational and latency limitations, and (ii) a self-learning scheme that uses the Multiverse-guided beam outcomes to enhance DL-based decision-making in the real world over time. Our work is distinguished from prior works as follows: First, we use a publicly available RF dataset collected from an autonomous car for creating different twins. Second, we present a framework with continuous interaction between the real world and Multiverse of twins at the edge, as opposed to a one-time emulation that is completed prior to actual deployment. Results reveal that Multiverse offers up to 79.43% and 85.22% top-10 beam selection accuracy for LOS and NLOS scenarios, respectively. Moreover, we observe 52.72-85.07% improvement in beam selection time compared to 802.11ad standard.

The rise of AI programs like ChatGPT has triggered a tidal wave of ethical handwringing, most prominently from within the industries that it threatens to destroy. After all, just because you can get a robot to instantly write code or write contracts or provide customer support for free, should you? Well, the answer from the Writers Guild of America is a qualified yes. This week, the Writers Guild of America proposed that ChatGPT would absolutely be allowed to write scripts in the future, provided that the credit (and the money) goes to the human writer who came up with the prompts in the first place. The proposal paints a scary picture of the future; a future in which even the most human of arts are crushed under the wheels of an unthinking technology.