Welcome to M5BMachine 5-Minute Briefing

Suppose Alice has collected a small number of samples from an unknown distribution, and would like to learn about the distribution. Bob, an untrusted data analyst, claims to have run a sophisticated data analysis on the distribution and makes assertions about its properties. When and how is it possi...

A property of functions is called location-invariant (or symmetric) if it can be characterized in terms of the frequencies in which each value occurs in the function, regardless of the locations in which each value occurs. It is known that the (query) complexity of testing location-invariant propert...

Apple Music serves listeners across 150+ storefronts in dozens of languages, with a catalog that grows by hundreds of thousands of new tracks daily. At this scale, search recall on misspelled, transliterated, and cross-lingual queries becomes a dominant driver of session quality, particularly for ta...

On-policy distillation offers dense, per-token supervision for training reasoning models; however, it remains unclear under which conditions this signal is beneficial and under which it is detrimental. Which teacher model should be used, and in the case of self-distillation, which specific context s...

Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of expert paths—the sequence of expert selections a token makes across all layers. This perspective reveals that, despite N^L po...

Apple is presenting new research at the annual IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), which takes place in person in Denver at the Colorado Convention Center from June 3 to June 7. We are proud to sponsor the conference, which brings together the scientific and indust...

Streaming vision-language models (VLMs) continuously generate responses given an instruction prompt and an online stream of input frames. This is a core mechanism for real-time visual assistants. Existing VLM frameworks predominantly assess models in offline settings. In contrast, the performance of...

At Apple, we believe privacy is a fundamental human right. As AI capabilities increase and become more integrated into people’s daily lives, advancing research in privacy-preserving techniques is increasingly important to ensure privacy is protected while users enjoy innovative AI experiences. Appl...

We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an efficient encoder-decoder architecture that compresses input views into a compact latent representation. This latent representation is then...

Image-based Joint-Embedding Predictive Architecture (I-JEPA) offers a promising approach to visual self-supervised learning through masked feature prediction. However with the inherent visual uncertainty at masked positions, feature prediction remains challenging and may fail to learn semantic repre...

One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despite this potential, a perceptual yet practical image codec is yet to be proposed. In this work, we aim...

Mixture-of-Experts (MoE) models enable sparse expert activation, meaning that only a subset of the model’s parameters is used during each inference. However, to translate this sparsity into practical performance, an expert caching mechanism is required. Previous works have proposed hardware-centric ...

Apple is presenting new research at the annual International Conference on Acoustics, Speech and Signal Processing (ICASSP), which takes place in person in Barcelona, Spain, from May 4 to 8. We are proud to again sponsor the conference, which brings together the scientific and industrial research co...

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...

Apple is presenting new research at the annual International Conference on Learning Representations (ICLR), which takes place in person in Rio de Janeiro, Brazil, from April 23 to 27. We are proud to again sponsor the conference, which brings together the scientific and industrial research communiti...

Apple is presenting new research at the annual ACM (Association of Computing Machinery) CHI Conference on Human Factors in Computing Systems, which takes place in person in Barcelona, Spain, from April 13 to 17. We are proud to again sponsor the conference, which brings together the scientific and i...

We introduce ProText, a dataset for measuring gendering and misgendering in stylistically diverse long-form English texts. ProText spans three dimensions: Theme nouns (names, occupations, titles, kinship terms), Theme category (stereotypically male, stereotypically female, gender-neutral/non-gendere...

Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such as 4K intractable. We introduce LGTM (Less Gaussians, Tex...

Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings. To address this, we systematically explore perceptual optimization strategies for 3DGS by searching over a di...

This paper was accepted at the Principled Design for Trustworthy AI — Interpretability, Robustness, and Safety across Modalities Workshop at ICLR 2026. What exactly makes a particular image unsafe? Systematically differentiating between benign and problematic images is a challenging problem, as subt...

We present AMES (Approximate Multimodal Enterprise Search), a unified multimodal late interaction retrieval architecture which is backend agnostic. AMES demonstrates that fine-grained multimodal late interaction retrieval can be deployed within a production grade enterprise search engine without arc...

Dense image captioning is critical for cross-modal alignment in vision-language pretraining and text-to-image generation, but scaling expert-quality annotations is prohibitively expensive. While synthetic captioning via strong vision-language models (VLMs) is a practical alternative, supervised dist...

Hand gesture classification using high-quality structured data such as videos, im- ages, and hand skeletons is a well-explored problem in computer vision. Alterna- tively, leveraging low-power, cost-effective bio-signals, e.g., surface electromyo- graphy (sEMG), allows for continuous gesture predict...

PyTorch \texttt{2.x} introduces a compiler designed to accelerate deep learning programs. However, for machine learning researchers, adapting to the PyTorch compiler to full potential can be challenging. The compiler operates at the Python bytecode level, making it appear as an opaque box. To addres...