Posts in category: Apple

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 important event for the community and to be an industry sponsor.
At the main conference and associated workshops, Apple researchers will present new research across a number of…Apple Machine Learning Research

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 direct access to the on-device foundation language model at the core of Apple Intelligence.
We crafted these generative models to power the wide range of…Apple Machine Learning Research

Tokenizer design significantly impacts language model performance,
yet evaluating tokenizer quality remains challenging. While text compression has emerged as a common intrinsic metric, recent work questions its reliability as a quality indicator. We investigate whether evaluating tokenizers on smaller models (350M parameters) reliably predicts their impact at larger scales (2.7B parameters).
Through experiments with established tokenizers from widely-adopted language models, we find that tokenizer choice minimally affects English tasks but yields significant, scale-consistent differences in…Apple Machine Learning Research

Vision foundation models pre-trained on massive data encode rich representations of real-world concepts, which can be adapted to downstream tasks by fine-tuning. However, fine-tuning foundation models on one task often leads to the issue of concept forgetting on other tasks. Recent methods of robust fine-tuning aim to mitigate forgetting of prior knowledge without affecting the fine-tuning performance. Knowledge is often preserved by matching the original and fine-tuned model weights or feature pairs. However, such point-wise matching can be too strong, without explicit awareness of the…Apple Machine Learning Research

Recent generations of frontier language models have introduced Large Reasoning Models
(LRMs) that generate detailed thinking processes before providing answers. While these models
demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scal-
ing properties, and limitations remain insufficiently understood. Current evaluations primarily fo-
cus on established mathematical and coding benchmarks, emphasizing final answer accuracy. How-
ever, this evaluation paradigm often suffers from data contamination and does not provide insights
into the reasoning traces’…Apple Machine Learning Research

Chain-of-thought (CoT) reasoning in vision language
models (VLMs) is crucial for improving
interpretability and trustworthiness. However,
current training recipes often relying on
datasets dominated by short annotations with
minimal rationales. In this work, we show that
training VLM on short answers leads to poor
generalization on reasoning tasks that require
more detailed explanations. To address this limitation,
we propose a two-stage post-training
strategy that extends the usage of short answer
data for enhanced CoT reasoning. First, we
augment short answers with CoT reasoning
generated by…Apple Machine Learning Research

Perceptual voice quality dimensions describe key characteristics of atypical speech and other speech modulations. Here we develop and evaluate voice quality models for seven voice and speech dimensions (intelligibility, imprecise consonants, harsh voice, naturalness, monoloudness, monopitch, and breathiness). Probes were trained on the public Speech Accessibility (SAP) project dataset with 11,184 samples from 434 speakers, using embeddings from frozen pre-trained models as features. We found that our probes had both strong performance and strong generalization across speech elicitation…Apple Machine Learning Research

Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingual data to include is unclear. Prior research often focuses on a small set of languages from a few language families or a single task. It is still an open question how these findings extend to a wider variety of languages and tasks. In this work, we contribute to this question by analyzing cross-lingual transfer for 263 languages from a wide variety of language families. Moreover, we include three popular NLP tasks…Apple Machine Learning Research

Apple Machine Learning Research

*Equal Contributors
Identifying mistakes (i.e., miscues) made while reading aloud is commonly approached post-hoc by comparing automatic speech recognition (ASR) transcriptions to the target reading text. However, post-hoc methods perform poorly when ASR inaccurately transcribes verbatim speech. To improve on current methods for reading error annotation, we propose a novel end-to-end architecture that incorporates the target reading text via prompting and is trained for both improved verbatim transcription and direct miscue detection. Our contributions include: first, demonstrating that…Apple Machine Learning Research