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Recurrent Neural Networks (RNNs) are naturally suited to efficient inference, requiring far less memory and compute than attention-based architectures, but the sequential nature of their computation has historically made it impractical to scale up RNNs to billions of parameters. A new advancement from Apple researchers makes RNN training dramatically more efficient — enabling large-scale training for the first time and widening the set of…
Apple is advancing AI and ML with fundamental research, much of which is shared through publications and engagement at conferences in order to accelerate progress in this important field and support the broader community. This week, the Fourteenth International Conference on Learning Representations (ICLR) will be held in Rio de Janeiro, Brazil, and Apple is proud to again participate in this important event for the research…
Apple Machine Learning Research at NeurIPS 2025
November 21, 2025
Apple researchers advance AI and ML through fundamental research, and to support the broader research community and help accelerate progress in this field, we share much of this work through publications and engagement at conferences.
Exploring LLMs with MLX and the Neural Accelerators in the M5 GPU
November 19, 2025
Mac with Apple silicon is increasingly popular among AI developers and researchers interested in using their Mac to experiment with the latest models and techniques. With MLX, users can explore and run LLMs efficiently on Mac. It allows researchers to experiment with new inference or fine-tuning techniques, or investigate AI techniques in a private environment, on their own hardware. MLX works with all Apple silicon systems, and with the latest…
The “Super Weight:” How Even a Single Parameter can Determine a Large Language Model’s Behavior
August 21, 2025
A recent paper from Apple researchers, “The Super Weight in Large Language Models,” reveals that an extremely small subset of parameters in LLMs (in some cases, a single parameter) can exert a disproportionate influence on an LLM’s overall functionality (see Figure 1). This work highlights the critical role of these “super weights” and their corresponding “super activations,”…
FastVLM: Efficient Vision Encoding for Vision Language Models
July 23, 2025research area Computer Vision
Vision Language Models (VLMs) enable visual understanding alongside textual inputs. They are typically built by passing visual tokens from a pretrained vision encoder to a pretrained Large Language Model (LLM) through a projection layer. By leveraging the rich visual representations of the vision encoder and the world knowledge and reasoning capabilities of the LLM, VLMs can be useful for a wide range of applications, including accessibility…
Apple Machine Learning Research at ICML 2025
July 11, 2025
Apple researchers are advancing AI and ML through fundamental research, and to support the broader research community and help accelerate progress in this field, we share much of this research through publications and engagement at conferences. Next week, the International Conference on Machine Learning (ICML) will be held in Vancouver, Canada, and Apple is proud to once again participate in this important event for the…
Apple Machine Learning Research at CVPR 2025
June 10, 2025research area Computer Vision, research area Methods and Algorithmsconference CVPR
Apple researchers are advancing AI and ML through fundamental research, and to support the broader research community and help accelerate progress in this field, we share much of our research through publications and engagement at conferences. This week, the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), will take place in Nashville, Tennessee. Apple is proud to once again participate in this…
With Apple Intelligence, we’re integrating powerful generative AI right into the apps and experiences people use every day, all while protecting their privacy. At the 2025 Worldwide Developers Conference we introduced a new generation of language foundation models specifically developed to enhance the Apple Intelligence features in our latest software releases. We also introduced the new Foundation Models framework, which gives app developers…
Ratings and reviews are an invaluable resource for users exploring an app on the App Store, providing insights into how others have experienced the app. With review summaries now available in iOS 18.4, users can quickly get a high-level overview of what other users think about an app, while still having the option to dive into individual reviews for more detail. This feature is powered by a novel, multi-step LLM-based system that periodically…
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