Apple Research

Apple researchers have published a study that looks into how LLMs can analyze audio and motion data to get a better overview of the user’s activities. Here are the details.

Apple has released Pico-Banana-400K, a highly curated 400,000-image research dataset which, interestingly, was built using Google’s Gemini-2.5 models. Here are the details.

Apple has published three interesting studies that offer some insight into how AI-based development could improve workflows, quality, and productivity. Here are the details.

In a new study, Apple researchers present a diffusion model that can write up to 128 times faster than its counterparts. Here’s how it works.

Today, Apple confirmed its participation in the 2025 International Conference on Computer Vision (ICCV), which will take place from October 19 to 23 in Honolulu. Here are the studies the company will present.

Google DeepMind’s work with AlphaFold has been nothing short of a miracle, but it is computationally expensive. With that in mind, Apple researchers set off to develop an alternative method to use AI to predict the 3D structure of proteins, and it shows promise. Here are the details.

A few months ago, Apple hosted a two-day event that featured talks and publications on the latest advancements in natural language processing (NLP). Today, the company published a post with multiple highlights, and all the studies presented. Here’s the roundup.

In a new study co-authored by Apple researchers, an open-source large language model (LLM) saw big performance improvements after being told to check its own work by using one simple productivity trick. Here are the details.

Apple researchers have developed an adapted version of the SlowFast-LLaVA model that beats larger models at long-form video analysis and understanding. Here’s what that means.

A few months ago, Apple hosted the Workshop on Privacy-Preserving Machine Learning, which featured presentations and discussions on privacy, security, and other key areas in responsible machine learning development. Now, it has made the presentations public. Here are three highlights.

In a new study, a group of Apple researchers describe a very interesting approach they took to, basically, get an open-source model to teach itself how to build good user interface code in SwiftUI. Here’s how they did it.

A new research paper from Apple details a technique that speeds up large language model responses, while preserving output quality. Here are the details.

Today, Apple published on its Machine Learning Research blog, select recordings from its 2024 Workshop on Human-Centered Machine Learning (HCML), highlighting its work on responsible AI development.

A new Apple-backed study, in collaboration with Aalto University in Finland, introduces ILuvUI: a vision-language model trained to understand mobile app interfaces from screenshots and from natural language conversations. Here’s what that means, and how they did it.

A new Apple-supported study argues that your behavior data (movement, sleep, exercise, etc.) can often be a stronger health signal than traditional biometric measurements like heart rate or blood oxygen. To prove it, the researchers developed a foundation model trained on behavioral data collected from wearables, and it performed surprisingly well. Here are the details.

There’s no shortage of rumors about Apple’s plans to release camera-equipped wearables. And while it’s easy to get fatigued by yet another wave of upcoming AI-powered hardware, one powerful use case often gets lost in the shuffle: accessibility.

Apple quietly dropped a new AI model on Hugging Face with an interesting twist. Instead of writing code like traditional LLMs generate text (left to right, top to bottom), it can also write out of order, and improve multiple chunks at once.

The result is faster code generation, at a performance that rivals top open-source coding models. Here’s how it works.

As AI agents come closer to taking real actions on our behalf (messaging someone, buying something, toggling account settings, etc.), a new study co-authored by Apple looks into how well do these systems really understand the consequences of their actions. Here’s what they found out.

Today, most generative image models basically fall into two main categories: diffusion models, like Stable Diffusion, or autoregressive models, like OpenAI’s GPT-4o. But Apple just released two papers that show how there might be room for a third, forgotten technique: Normalizing Flows. And with a dash of Transformers on top, they might be more capable than previously thought.

As more users start relying on AI for writing tasks like email drafts and document summaries, one common frustration remains: the output often sounds way too generic. Even when models like ChatGPT or Gemini are given detailed prompts, they rarely nail a user’s individual tone or voice without plenty of manual tweaking. Apple is now proposing a solution.

There’s a new Apple research paper making the rounds, and if you’ve seen the reactions, you’d think it just toppled the entire LLM industry. That is far from true, although it might be the best attempt to bring to the mainstream a discussion that the ML community has been having for ages. Here is why this paper matters.