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AppleEdge AINewsApple researchers ran a week-long study with Wizard of Oz probes to see how people define their own machine learning phenomena, identifying four sites where users negotiate ontological boundaries in personalized sensing systems.
Read the article ↗AppleInferenceNewsApple researchers show that adding language discrimination during pretraining helps bilingual HuBERT models match monolingual performance on phone, lexical, and prosodic measures, with the biggest gains when applied early.
Read the article ↗Meta introduced Muse Glimmer, an open-weight 30B model for on-device agentic AI, with ExecuTorch support for NVIDIA GPUs and Apple silicon. Prebuilt PTE artifacts are available, enabling text and image inputs, DFlash speculative decoding, and 128K+ token context.
Read the article ↗The PyTorch Foundation's six hosted projects reported quarterly progress: PyTorch 2.13 with Apple Silicon FlexAttention, vLLM's Model Runner V2 redesign, DeepSpeed's Ulysses integration, Ray's GB200 support, Helion's cross-hardware kernels, and Safetensors' GIL-free serialization.
Read the article ↗AppleInferenceNewsApple researchers show that frame-level activations in pretrained speech encoders can detect code-switching without transcription, using language-selective hidden dimensions. The method reaches a mean F1 of 0.798 across 14 language pairs in CS-FLEURS with a Whisper-small encoder.
Read the article ↗AppleInferenceNewsApple researchers introduce the first tri-modal masked diffusion model pretrained from scratch on text, image-text, and audio-text data, with a new SDE-based reparameterization that removes batch-size tuning.
Read the article ↗AppleInferenceAnalysisApple researchers compared auxiliary-language mixing against hyperparameter tuning across four model scales up to 1.43B parameters, finding mixing improves validation loss and downstream accuracy more, with gains equivalent to 2-13x unique target data.
Read the article ↗AppleInferenceNewsApple researchers propose SELFCOMPACT, a scaffold that lets language models invoke a compaction tool guided by a lightweight rubric, matching or beating fixed-interval summarization at lower token cost on math and agentic search benchmarks.
Read the article ↗AppleInferenceNewsApple researchers describe Minions, a system that aggregates idle Apple Silicon GPUs across macOS devices to run large-scale machine learning inference when centralized GPU resources are constrained.
Read the article ↗AppleInferenceNewsApple researchers compared LLM and human annotators on MQM and Error Span Annotation across 70 language pairs and WMT23/WMT25 data, finding agreement varies by scheme, language and domain, with both sides facing distinct failure modes.
Read the article ↗AppleInferenceNewsApple researchers propose Normalizing Trajectory Models, which use conditional normalizing flows per reverse step to retain exact likelihood while generating high-quality images in four sampling steps, matching or beating strong baselines.
Read the article ↗AppleInferenceNewsApple researchers propose RISED, a method that uses rubric-based LLM judge profiles to select training data, supervise policy via self-distillation, and steer rollouts away from failure modes in multi-environment RL training.
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